{"id":26498,"date":"2026-07-10T02:45:21","date_gmt":"2026-07-10T02:45:21","guid":{"rendered":"https:\/\/umang.pk\/2026\/07\/10\/%d9%be%d8%b1%d9%88%da%88%da%a9%d9%b9-%da%a9%d8%a7-%d8%aa%d8%ac%d8%b1%d8%a8%db%81-%d8%a7%d9%86%db%81%d8%a7%d9%86%d8%b3%d9%85%d9%86%d9%b9-%d9%85%d8%a7%da%88%d9%84%d9%86%da%af-%da%a9%db%92-%d8%b3%d8%a7\/"},"modified":"2026-07-10T02:45:22","modified_gmt":"2026-07-10T02:45:22","slug":"%d9%be%d8%b1%d9%88%da%88%da%a9%d9%b9-%da%a9%d8%a7-%d8%aa%d8%ac%d8%b1%d8%a8%db%81-%d8%a7%d9%86%db%81%d8%a7%d9%86%d8%b3%d9%85%d9%86%d9%b9-%d9%85%d8%a7%da%88%d9%84%d9%86%da%af-%da%a9%db%92-%d8%b3%d8%a7","status":"publish","type":"post","link":"https:\/\/umang.pk\/ur\/2026\/07\/10\/%d9%be%d8%b1%d9%88%da%88%da%a9%d9%b9-%da%a9%d8%a7-%d8%aa%d8%ac%d8%b1%d8%a8%db%81-%d8%a7%d9%86%db%81%d8%a7%d9%86%d8%b3%d9%85%d9%86%d9%b9-%d9%85%d8%a7%da%88%d9%84%d9%86%da%af-%da%a9%db%92-%d8%b3%d8%a7\/","title":{"rendered":"\u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u0627 \u062a\u062c\u0631\u0628\u06c1 \u0627\u0646\u06c1\u0627\u0646\u0633\u0645\u0646\u0679 \u0645\u0627\u0688\u0644\u0646\u06af \u06a9\u06d2 \u0633\u0627\u062a\u06be: LLM \u0641\u06cc\u0686\u0631 \u0631\u0648\u0644 \u0622\u0624\u0679 \u06a9\u0648 \u0627\u0646 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06c1\u062f\u0641 \u0628\u0646\u0627\u0646\u0627 \u062c\u0648 \u0627\u0635\u0644 \u0645\u06cc\u06ba \u0641\u0627\u0626\u062f\u06c1 \u0627\u0679\u06be\u0627\u0626\u06cc\u06ba \u06af\u06d2 (Python \u0646\u0641\u0627\u0630)"},"content":{"rendered":"\n
\n

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\u0627\u0639\u0644\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc CATE \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u062e\u0635\u0648\u0635\u06cc\u062a \u062d\u0627\u0635\u0644 \u06c1\u0648\u062a\u06cc \u06c1\u06d2\u06d4 0 \u06a9\u06d2 \u0642\u0631\u06cc\u0628 CATE \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u0686\u06be\u0648\u0691 \u062f\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0646\u062a\u06cc\u062c\u06c1 \u0627\u06cc\u06a9 \u062f\u0627\u0646\u06d2 \u062f\u0627\u0631 \u0631\u0648\u0644 \u0622\u0624\u0679 \u06c1\u06d2 \u062c\u0648 \u0639\u0644\u0627\u062c \u067e\u0631 \u062a\u0648\u062c\u06c1 \u0645\u0631\u06a9\u0648\u0632 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0648 \u062d\u0642\u06cc\u0642\u06cc \u0642\u062f\u0631 \u06a9\u0648 \u0628\u0691\u06be\u0627\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u0644\u0627\u06af\u062a \u0627\u0648\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06cc \u0627\u0644\u062c\u06be\u0646 \u06a9\u0648 \u062d\u0642\u06cc\u0642\u06cc \u0641\u0648\u0627\u0626\u062f \u06a9\u06d2 \u0645\u062a\u0646\u0627\u0633\u0628 \u0631\u06a9\u06be\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n

ML \u0627\u0646\u062c\u06cc\u0646\u0626\u0631\u0632 \u0627\u0648\u0631 \u067e\u0631\u0648\u0688\u06a9\u0679 \u0688\u06cc\u0679\u0627 \u0633\u0627\u0626\u0646\u0633\u062f\u0627\u0646\u0648\u06ba \u06a9\u06d2 \u0644\u06cc\u06d2 \u062c\u0648 \u062d\u0633\u0628 \u0636\u0631\u0648\u0631\u062a AI \u0631\u0648\u0644 \u0622\u0624\u0679\u0633 \u06a9\u0648 \u062a\u0631\u062a\u06cc\u0628 \u062f\u06d2 \u0631\u06c1\u06d2 \u06c1\u06cc\u06ba\u060c \u06cc\u06c1 \u06af\u0627\u0626\u06cc\u0688 \u0622\u067e \u06a9\u0648 scikit-learn \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0634\u0631\u0648\u0639 \u0633\u06d2 \u06c1\u06cc \u0645\u0627\u0688\u0644\u0646\u06af \u0645\u06cc\u06ba \u0628\u06c1\u062a\u0631\u06cc \u0644\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0631\u06c1\u0646\u0645\u0627\u0626\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u06c1\u0645 \u0627\u0633\u06d2 \u0636\u0631\u0648\u0631\u062a \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0627\u0646\u062d\u0635\u0627\u0631 \u062c\u06cc\u0633\u06d2 causalml \u06cc\u0627 econml \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u0628\u0646\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u062a\u0627\u06a9\u06c1 \u0622\u067e \u0628\u0646\u06cc\u0627\u062f\u06cc \u0645\u06cc\u06a9\u0627\u0646\u0632\u0645 \u06a9\u0648 \u0633\u0645\u062c\u06be \u0633\u06a9\u06cc\u06ba\u06d4<\/p>\n

\u06c1\u0645 \u062f\u0648 \u0645\u06cc\u0679\u0627 \u0644\u0631\u0646\u0631 \u0627\u067e\u0631\u0648\u0686\u0632 \u06a9\u0648 \u0646\u0627\u0641\u0630 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633 \u0628\u0627\u062a \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 Qini \u0648\u06a9\u0631 \u0628\u0646\u0627\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06c1 \u0645\u0627\u0688\u0644 \u06a9\u0633 \u062d\u062f \u062a\u06a9 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u062a\u0642\u0633\u06cc\u0645 \u0634\u062f\u06c1 \u0631\u06cc\u0644\u06cc\u0632 \u06a9\u06d2 \u0641\u06cc\u0635\u0644\u06d2 \u06a9\u0627 \u0627\u0635\u0648\u0644 \u0644\u06a9\u06be\u062a\u0627 \u06c1\u06d2\u06d4 \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 50,000 \u0635\u0627\u0631\u0641 \u06a9\u06d2 SaaS \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u0648 \u0645\u062e\u062a\u0644\u0641 \u0645\u0646\u06af\u0646\u06cc \u06a9\u06d2 \u062f\u0631\u062c\u0627\u062a \u0645\u06cc\u06ba \u0645\u062a\u0641\u0627\u0648\u062a \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0646\u0642\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n

\u0622\u062e\u0631 \u062a\u06a9\u060c \u0622\u067e \u0633\u0645\u062c\u06be \u062c\u0627\u0626\u06cc\u06ba \u06af\u06d2 \u06a9\u06c1 \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u067e\u0631 \u06a9\u0628 \u0628\u06be\u0631\u0648\u0633\u06c1 \u06a9\u0631\u0646\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u0627\u067e\u0646\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u0627\u06cc\u06a9 \u062d\u0642\u06cc\u0642\u06cc \u062a\u0639\u06cc\u0646\u0627\u062a\u06cc \u067e\u0627\u0644\u06cc\u0633\u06cc \u0645\u06cc\u06ba \u06a9\u06cc\u0633\u06d2 \u062a\u0631\u062c\u0645\u06c1 \u06a9\u0631\u0646\u0627 \u06c1\u06d2\u06d4<\/p>\n

\u0627\u0646\u0688\u06cc\u06a9\u0633<\/h2>\n

AI \u0630\u0627\u062a\u06cc \u0646\u0648\u0639\u06cc\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u0639\u0644\u0627\u062c \u06a9\u06cc \u0627\u0648\u0633\u0637 \u062a\u0627\u062b\u06cc\u0631 \u06a9\u06cc\u0648\u06ba \u06af\u0645\u0631\u0627\u06c1 \u06a9\u0646 \u06c1\u06d2\u06d4<\/h2>\n

\u0627\u0633 \u0628\u0627\u062a \u067e\u0631 \u063a\u0648\u0631 \u06a9\u0631\u06cc\u06ba \u06a9\u06c1 \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u0648\u0633\u0637 \u0627\u062b\u0631 \u0627\u0635\u0644 \u0645\u06cc\u06ba \u06a9\u06cc\u0627 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06cc\u06a9 \u0639\u0627\u0645 SaaS \u067e\u0631\u0648\u0688\u06a9\u0679 \u0645\u06cc\u06ba\u060c \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u0622\u067e\u0679 \u0627\u0646 \u062a\u062c\u0631\u0628\u0627\u062a \u0645\u06cc\u06ba \u0632\u06cc\u0627\u062f\u06c1 \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u062f\u06cc \u062c\u0627\u062a\u06cc \u06c1\u06d2 \u06a9\u06cc\u0648\u0646\u06a9\u06c1 \u0648\u06c1 \u0646\u0626\u06cc \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u0648 \u06a9\u062b\u0631\u062a \u0633\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u062e\u0648\u062f \u06a9\u0648 \u06a9\u0645 \u0633\u0645\u062c\u06be\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06cc\u0648\u0646\u06a9\u06c1 \u0648\u06c1 \u0679\u0648\u06af\u0644 \u06a9\u0648 \u0646\u0638\u0631 \u0627\u0646\u062f\u0627\u0632 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n

\u0627\u0648\u0633\u0637 \u0627\u062b\u0631 \u062a\u062c\u0631\u0628\u06c1 \u0645\u06cc\u06ba \u062d\u0635\u06c1 \u0644\u06cc\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0627\u062e\u062a\u0644\u0627\u0637 \u06a9\u06cc \u0639\u06a9\u0627\u0633\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u0627\u0648\u0631 \u06cc\u06c1 \u0645\u0631\u06a9\u0628 \u0645\u0645\u06a9\u0646\u06c1 \u0637\u0648\u0631 \u067e\u0631 \u0628\u0627\u0644\u06a9\u0644 \u0648\u06cc\u0633\u0627 \u0646\u06c1\u06cc\u06ba \u06c1\u0648\u06af\u0627 \u062c\u06cc\u0633\u0627 \u06a9\u06c1 \u0639\u0627\u0645 \u0622\u0628\u0627\u062f\u06cc \u06a9\u0648 \u0645\u06a9\u0645\u0644 \u0644\u0627\u0646\u0686 \u06a9\u06d2 \u0648\u0642\u062a \u0633\u0627\u0645\u0646\u0627 \u06a9\u0631\u0646\u0627 \u067e\u0691\u0627\u06d4<\/p>\n

\u0632\u06cc\u0627\u062f\u06c1 \u0627\u06c1\u0645 \u0628\u0627\u062a \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u0648\u0633\u0637 \u0627\u062b\u0631 \u0630\u06cc\u0644\u06cc \u06af\u0631\u0648\u067e\u0648\u06ba \u0645\u06cc\u06ba \u0639\u0644\u0627\u062c \u06a9\u06d2 \u0627\u062b\u0631 \u06a9\u06cc \u0633\u0645\u062a \u06a9\u0648 \u062f\u06be\u0646\u062f\u0644\u0627 \u062f\u06cc\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n

\u0627\u06cc\u06a9 \u0627\u06cc\u0633\u06d2 \u0645\u0646\u0638\u0631 \u0646\u0627\u0645\u06d2 \u067e\u0631 \u063a\u0648\u0631 \u06a9\u0631\u06cc\u06ba \u062c\u06c1\u0627\u06ba AI \u0633\u0645\u0631\u06cc \u0641\u06cc\u0686\u0631 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0635\u0631\u0641 9.6 \u0641\u06cc\u0635\u062f \u067e\u0648\u0627\u0626\u0646\u0679 \u0644\u0641\u0679\u060c \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 7.4 \u0641\u06cc\u0635\u062f \u067e\u0648\u0627\u0626\u0646\u0679 \u0644\u0641\u0679\u060c \u0627\u0648\u0631 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 6.7 \u0641\u06cc\u0635\u062f \u067e\u0648\u0627\u0626\u0646\u0679 \u0644\u0641\u0679 \u067e\u06cc\u062f\u0627 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0633 \u06a9\u0627 \u0627\u0648\u0633\u0637 \u06a9\u0633\u06cc \u0627\u06cc\u0633\u06cc \u0686\u06cc\u0632 \u0633\u06d2 \u06c1\u0648\u062a\u0627 \u06c1\u06d2 \u062c\u0648 \u06cc\u06a9\u0633\u0627\u06ba \u0637\u0648\u0631 \u067e\u0631 \u0645\u062b\u0628\u062a \u0646\u0638\u0631 \u0622\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n

\u062a\u0627\u06c1\u0645\u060c \u06cc\u06c1\u0627\u06ba \u0627\u0633\u0679\u0631\u06cc\u0679\u062c\u06a9 \u0636\u0631\u0648\u0631\u062a \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u067e\u0631 \u0631\u0648\u0644 \u0622\u0624\u0679 \u067e\u0631 \u062a\u0648\u062c\u06c1 \u0645\u0631\u06a9\u0648\u0632 \u06a9\u06cc \u062c\u0627\u0626\u06d2 \u062c\u0628\u06a9\u06c1 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc \u0646\u06af\u0631\u0627\u0646\u06cc \u06a9\u06cc \u062c\u0627\u0626\u06d2 \u062a\u0627\u06a9\u06c1 \u06cc\u06c1 \u06cc\u0642\u06cc\u0646\u06cc \u0628\u0646\u0627\u06cc\u0627 \u062c\u0627 \u0633\u06a9\u06d2 \u06a9\u06c1 \u0622\u067e\u0679\u0645\u0627\u0626\u0632\u0688 \u0648\u0631\u06a9 \u0641\u0644\u0648 \u0645\u06cc\u06ba \u062e\u0644\u0644 \u0646\u06c1 \u067e\u0691\u06d2\u06d4 \u0634\u067e\u0646\u06af \u0627\u0633 \u067e\u06be\u06cc\u0644\u0627\u0624 \u06a9\u0648 \u0645\u06a9\u0645\u0644 \u0637\u0648\u0631 \u067e\u0631 \u0646\u0638\u0631 \u0627\u0646\u062f\u0627\u0632 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n

\u06cc\u06c1 \u0646\u0645\u0648\u0646\u06c1 AI \u0635\u0644\u0627\u062d\u06cc\u062a\u0648\u06ba \u06a9\u06d2 \u062a\u0645\u0627\u0645 \u0632\u0645\u0631\u0648\u06ba \u0645\u06cc\u06ba \u0638\u0627\u06c1\u0631 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0646\u0679\u0631\u067e\u0631\u0627\u0626\u0632 \u0679\u06cc\u0645\u0648\u06ba \u06a9\u06d2 \u0644\u06cc\u06d2 AI \u0645\u06cc\u0679\u0646\u06af \u0633\u0645\u0631\u06cc\u0632\u0631 \u067e\u0631 \u063a\u0648\u0631 \u06a9\u0631\u06cc\u06ba\u06d4 \u0646\u0626\u06d2 \u062c\u0648\u0627\u0626\u0646 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u0648\u06ba \u06a9\u0648 \u0644\u0645\u0628\u06d2 \u062f\u06be\u0627\u06af\u0648\u06ba \u06a9\u0648 \u0628\u0631\u0642\u0631\u0627\u0631 \u0631\u06a9\u06be\u0646\u06d2 \u0645\u06cc\u06ba \u062f\u0634\u0648\u0627\u0631\u06cc \u06a9\u0627 \u0633\u0627\u0645\u0646\u0627 \u06c1\u06d2\u06d4 \u0679\u06cc\u0645 \u06a9\u06d2 \u062a\u062c\u0631\u0628\u06c1 \u06a9\u0627\u0631 \u0627\u0631\u06a9\u0627\u0646 \u062c\u0648 \u06a9\u06c1 AI \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u062a\u06cc\u0632\u06cc \u0633\u06d2 \u067e\u0691\u06be\u062a\u06d2 \u06c1\u06cc\u06ba \u0644\u06a9\u06be \u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0646 \u06a9\u0627 \u062e\u0644\u0627\u0635\u06c1 \u0633\u0633\u062a \u067e\u0691 \u0631\u06c1\u0627 \u06c1\u06d2\u06d4 \u0627\u06cc\u06a9 \u0645\u062b\u0628\u062a \u0627\u0648\u0633\u0637 \u06a9\u0633\u06cc \u062e\u0635\u0648\u0635\u06cc\u062a \u06a9\u06cc \u062a\u0639\u0645\u06cc\u0631 \u06a9\u0627 \u062c\u0648\u0627\u0632 \u067e\u06cc\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u0627\u0633\u06d2 \u062a\u0645\u0627\u0645 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0645\u06cc\u06ba \u06cc\u06a9\u0633\u0627\u06ba \u0637\u0648\u0631 \u067e\u0631 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0628\u0627\u0631\u06d2 \u0645\u06cc\u06ba \u06a9\u0686\u06be \u0646\u06c1\u06cc\u06ba \u06a9\u06c1\u062a\u0627\u06d4<\/p>\n

\u0627\u0636\u0627\u0641\u06c1 \u0645\u0627\u0688\u0644\u0646\u06af CATE \u06a9\u0627 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u0644\u06af\u0627 \u06a9\u0631 \u0627\u0633 \u0645\u0633\u0626\u0644\u06d2 \u06a9\u0648 \u062d\u0644 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u060c \u06a9\u0633\u06cc \u062e\u0627\u0635 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u062a\u0648\u0642\u0639 \u0639\u0644\u0627\u062c \u06a9\u06d2 \u0627\u062b\u0631\u060c \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u0648 \u0645\u062f\u0646\u0638\u0631 \u0631\u06a9\u06be\u062a\u06d2 \u06c1\u0648\u0626\u06d2\u06d4 \u0628\u06c1\u062a \u0645\u062b\u0628\u062a CATE \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0639\u0644\u0627\u062c \u062d\u0627\u0635\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u062c\u0628\u06a9\u06c1 \u06a9\u0645 CATE \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u0631\u0648\u06a9 \u062f\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0622\u067e \u0645\u0631\u062d\u0644\u06c1 3 \u0645\u06cc\u06ba \u062c\u0648 Qini \u0648\u06a9\u0631 \u0628\u0646\u0627\u0626\u06cc\u06ba \u06af\u06d2 \u0648\u06c1 \u0622\u067e \u06a9\u0648 \u0628\u062a\u0627\u0626\u06d2 \u06af\u0627 \u06a9\u06c1 \u0635\u0631\u0641 \u0627\u0639\u0644\u06cc CATE \u0648\u0627\u0644\u06d2 \u062d\u0635\u0648\u06ba \u067e\u0631 \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u0631\u06a9\u06d2 \u0627\u0648\u0631 \u0628\u0627\u0642\u06cc \u06a9\u0648 \u0686\u06be\u0648\u0691 \u06a9\u0631 \u06a9\u062a\u0646\u06cc \u0642\u06cc\u0645\u062a \u062d\u0627\u0635\u0644 \u06a9\u06cc \u062c\u0627 \u0633\u06a9\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n

\u0628\u06c1\u062a\u0631 \u0645\u0627\u0688\u0644\u0646\u06af \u062f\u0631\u0627\u0635\u0644 \u06a9\u06cc\u0627 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/h2>\n

\u0627\u0636\u0627\u0641\u06c1 \u0645\u0627\u0688\u0644\u0646\u06af causal inference \u067e\u0631 \u0628\u0646\u0627\u06cc\u0627 \u06af\u06cc\u0627 \u06c1\u06d2\u06d4 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0645\u0642\u062f\u0627\u0631 \u0627\u0646\u0641\u0631\u0627\u062f\u06cc \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u062b\u0631 \u06c1\u06d2\u060c \u062c\u0648 \u06a9\u0633\u06cc \u062e\u0627\u0635 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u0645\u06a9\u0646\u06c1 \u0646\u062a\u0627\u0626\u062c \u0645\u06cc\u06ba \u0641\u0631\u0642 \u06a9\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n

ITE(i) = Y_i(1) - Y_i(0)\n<\/code><\/pre>\n
Y_i(1)<\/code>    \u0622\u067e \u06a9\u0633 \u0642\u0633\u0645 \u06a9\u06d2 \u0635\u0627\u0631\u0641 \u06c1\u06cc\u06ba\u061f i<\/code> \u0627\u0633 \u06a9\u0627 \u062a\u0639\u0644\u0642 \u0641\u0639\u0627\u0644\u06cc\u062a \u0633\u06d2 \u06c1\u06d2\u06d4 Y_i(0)<\/code> \u0622\u067e \u06a9\u0633 \u0642\u0633\u0645 \u06a9\u06d2 \u0635\u0627\u0631\u0641 \u06c1\u06cc\u06ba\u061f i<\/code> \u0645\u06cc\u06ba \u0627\u0633 \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u06a9\u0631\u0648\u06ba \u06af\u0627\u06d4 \u0645\u0633\u0626\u0644\u06c1 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0627\u06cc\u06a9 \u062e\u0627\u0635 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0645 \u0635\u0631\u0641 \u062f\u0648 \u0645\u0642\u062f\u0627\u0631\u0648\u06ba \u0645\u06cc\u06ba \u0633\u06d2 \u0627\u06cc\u06a9 \u06a9\u0627 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba: Y_i(1)<\/code> \u0639\u0644\u0627\u062c \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0627\u0648\u0631 Y_i(0)<\/code> \u06a9\u0646\u0679\u0631\u0648\u0644 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0631 \u0635\u0627\u0631\u0641 \u0635\u0631\u0641 \u0627\u06cc\u06a9 \u0634\u0639\u0628\u06d2 \u0645\u06cc\u06ba \u0638\u0627\u06c1\u0631 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
CATE \u0622\u0628\u0627\u062f\u06cc \u06a9\u06cc \u0633\u0637\u062d \u06a9\u0627 \u0627\u06cc\u06a9 \u0627\u06cc\u0646\u0627\u0644\u0627\u06af \u06c1\u06d2\u060c \u0635\u0627\u0631\u0641 \u06a9\u06cc \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u06cc \u0628\u0646\u06cc\u0627\u062f \u067e\u0631 \u0645\u062a\u0648\u0642\u0639 \u0627\u0646\u0641\u0631\u0627\u062f\u06cc \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u062b\u0631\u06d4<\/p>\n
CATE(x) = E[Y(1) - Y(0) | X = x]\n<\/code><\/pre>\n
\u0645\u06cc\u0679\u0627 \u0644\u0631\u0646\u0631 \u0627\u067e\u0631\u0648\u0686 CATE \u06a9\u0627 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u0639\u0644\u0627\u062c \u0627\u0648\u0631 \u06a9\u0646\u0679\u0631\u0648\u0644 \u06af\u0631\u0648\u067e\u0633 \u0645\u06cc\u06ba \u0627\u0644\u06af \u0627\u0644\u06af \u0646\u062a\u0627\u0626\u062c \u06a9\u06d2 \u0645\u0627\u0688\u0644\u0632 \u06a9\u0648 \u0644\u0627\u06af\u0648 \u06a9\u0631\u06a9\u06d2 \u0627\u0648\u0631 \u067e\u06be\u0631 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc\u0648\u06ba \u0645\u06cc\u06ba \u0641\u0631\u0642 \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0644\u06af\u0627 \u06a9\u0631 \u0644\u06af\u0627\u062a\u0627 \u06c1\u06d2\u06d4 T-learner \u0627\u0648\u0631 X-learner (K\u00fcnzel et al.) \u062f\u0648\u0646\u0648\u06ba \u0634\u0646\u0627\u062e\u062a \u06a9\u06d2 \u062a\u06cc\u0646 \u0645\u0641\u0631\u0648\u0636\u0648\u06ba \u067e\u0631 \u0645\u0628\u0646\u06cc \u06c1\u06cc\u06ba\u06d4<\/p>\n
    \n
  1. \n
    \u0645\u0628\u06c1\u0645 \u0646\u06c1\u06cc\u06ba<\/strong> (\u0645\u0634\u0631\u0648\u0637 ignorability): \u0639\u0644\u0627\u062c \u06a9\u06cc \u062a\u0641\u0648\u06cc\u0636 \u06a9\u0627 \u062a\u0639\u06cc\u0646 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 covariates T \u22a5 (Y(0)\u060c Y(1)) \u0633\u06d2 \u06a9\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2 | X. \u0628\u06d2 \u062a\u0631\u062a\u06cc\u0628 \u062a\u062c\u0631\u0628\u0627\u062a \u0645\u06cc\u06ba\u060c \u06cc\u06c1 \u062e\u0648\u062f \u0628\u062e\u0648\u062f \u0628\u0631\u0642\u0631\u0627\u0631 \u0631\u06c1\u062a\u0627 \u06c1\u06d2\u06d4 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a\u06cc \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06d2 \u0645\u0637\u0627\u0644\u0639\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0644\u062c\u06be\u0627\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0639\u0648\u0627\u0645\u0644 \u067e\u0631 \u0642\u0627\u0628\u0648 \u067e\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0641\u06cc\u0686\u0631 \u0633\u06cc\u0679 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u0648\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<\/li>\n
  2. \n
    \u0646\u0642\u0644<\/strong> (\u0645\u062b\u0628\u062a\u06cc\u062a): \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u067e\u0627\u0633 \u0639\u0644\u0627\u062c \u06cc\u0627 \u06a9\u0646\u0679\u0631\u0648\u0644 \u062d\u0627\u0635\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u0627 \u063a\u06cc\u0631 \u0635\u0641\u0631 \u0627\u0645\u06a9\u0627\u0646 \u06c1\u0648\u062a\u0627 \u06c1\u06d2 (0 < P(T=1|X=x) < 1)\u06d4 \u0627\u06af\u0631 \u06a9\u0686\u06be \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u0639\u0627\u06c1\u062f\u06d2 \u06a9\u0627 \u0627\u0645\u06a9\u0627\u0646 \u062a\u0642\u0631\u06cc\u0628\u0627\u064b \u0635\u0641\u0631 \u06c1\u06d2 (12%\u060c \u062c\u06cc\u0633\u0627 \u06a9\u06c1 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0627\u0633 \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0645\u06cc\u06ba \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba)\u060c \u062a\u0648 \u0627\u0633 \u062e\u0637\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 CATE \u06a9\u06d2 \u062a\u062e\u0645\u06cc\u0646\u06d2 \u0645\u06cc\u06ba \u0632\u06cc\u0627\u062f\u06c1 \u0641\u0631\u0642 \u06c1\u0648\u06af\u0627\u06d4<\/p>\n<\/li>\n
  3. \n
    \u0633\u0648\u0679 \u0628\u0627\u0631<\/strong>: \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u0627 \u0627\u0646\u062d\u0635\u0627\u0631 \u0635\u0631\u0641 \u0627\u0646 \u06a9\u06d2 \u0627\u067e\u0646\u06d2 \u0639\u0644\u0627\u062c \u067e\u0631 \u06c1\u06d2\u060c \u0642\u0637\u0639 \u0646\u0638\u0631 \u0627\u0633 \u06a9\u06d2 \u06a9\u06c1 \u0627\u0646 \u06a9\u06d2 \u0622\u0633 \u067e\u0627\u0633 \u06a9\u06d2 \u062f\u0648\u0633\u0631\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc\u0627 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0627\u0633 \u0645\u0641\u0631\u0648\u0636\u06d2 \u06a9\u06cc \u062e\u0644\u0627\u0641 \u0648\u0631\u0632\u06cc \u06c1\u0648 \u0633\u06a9\u062a\u06cc \u06c1\u06d2 \u0627\u06af\u0631 \u0635\u0627\u0631\u0641 \u0627\u067e\u0646\u06d2 \u0648\u0631\u06a9 \u0627\u0633\u067e\u06cc\u0633 \u06cc\u0627 \u0633\u0645\u0627\u062c\u06cc \u06af\u0631\u0627\u0641 \u06a9\u0627 \u0627\u0634\u062a\u0631\u0627\u06a9 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba (\u062f\u06cc\u06a9\u06be\u06cc\u06ba "\u0622\u06af\u06d2 \u06a9\u06cc\u0627 \u06a9\u0631\u0646\u0627 \u06c1\u06d2”)\u06d4<\/p>\n<\/li>\n<\/ol>\n
    \u0634\u0631\u0637\u06cc\u06ba<\/h2>\n
    \u0622\u067e \u06a9\u0648 \u062f\u0631\u062c \u0630\u06cc\u0644 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u0648\u06af\u06cc:<\/p>\n
    \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u06a9\u06d2 \u0644\u06cc\u06d2 \u067e\u06cc\u06a9\u062c\u0632 \u0627\u0646\u0633\u0679\u0627\u0644 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n
    pip install numpy pandas scikit-learn matplotlib scipy\n<\/code><\/pre>\n
    \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u06cc\u06c1 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644\u0632 \u06a9\u06d2 \u0645\u06a9\u0645\u0644 \u0646\u0645\u0628\u0631 \u0627\u0633\u0679\u06cc\u06a9 \u06a9\u0648 \u0627\u0646\u0633\u0679\u0627\u0644 \u06a9\u0631\u06d2 \u06af\u0627\u06d4 \u0645\u062c\u06be\u06d2 \u0686\u0627\u0631\u0679 \u062c\u0646\u0631\u06cc\u0679\u0631 \u0645\u06cc\u06ba KDE \u06a9\u06d2 \u0633\u0627\u062a\u06be Qini \u0645\u0646\u062d\u0646\u06cc \u062e\u0637\u0648\u0637 \u06a9\u0648 \u06c1\u0645\u0648\u0627\u0631 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0633\u06a9\u0627\u0626\u067e\u06cc \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2\u06d4 \u0628\u0627\u0642\u06cc \u0633\u0628 \u06a9\u0686\u06be \u0645\u0639\u06cc\u0627\u0631\u06cc ML \u0679\u0648\u0644\u0632 \u06c1\u06cc\u06ba\u06d4<\/p>\n
    \u0645\u0635\u0646\u0648\u0639\u06cc \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u062d\u0627\u0635\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0633\u0627\u062a\u06be\u06cc \u0631\u06cc\u067e\u0648\u0632\u0679\u0631\u06cc \u06a9\u0648 \u06a9\u0644\u0648\u0646 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n
    git clone https:\/\/github.com\/RudrenduPaul\/product-experimentation-causal-inference-genai-llm.git\ncd product-experimentation-causal-inference-genai-llm\npython data\/generate_data.py --seed 42 --n-users 50000 --out data\/synthetic_llm_logs.csv\n<\/code><\/pre>\n
    \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u0688\u06cc\u0679\u0627 \u062c\u0646\u0631\u06cc\u0679\u0631 50,000 \u0645\u0635\u0646\u0648\u0639\u06cc SaaS \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0627 \u0627\u06cc\u06a9 \u0642\u0627\u0628\u0644 \u062a\u0648\u0644\u06cc\u062f \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u0628\u0646\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u067e\u0627\u0633 \u0645\u0646\u06af\u0646\u06cc \u06a9\u06cc \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc (\u06a9\u0645\u060c \u062f\u0631\u0645\u06cc\u0627\u0646\u06cc\u060c \u0632\u06cc\u0627\u062f\u06c1)\u060c \u0627\u0633\u062a\u0641\u0633\u0627\u0631 \u06a9\u0627 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0627 \u0633\u06a9\u0648\u0631\u060c \u0627\u0648\u0631 AI \u0633\u0645\u0631\u06cc \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u0622\u067e\u0679 \u0627\u0646 \u062c\u06be\u0646\u0688\u0627 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u0622\u067e\u0679 \u0627\u0646 \u06a9\u0627 \u0632\u0645\u06cc\u0646\u06cc \u0633\u0686\u0627\u0626\u06cc \u06a9\u0627\u0632\u0644 \u0627\u062b\u0631 \u062a\u0642\u0631\u06cc\u0628\u0627\u064b +8 \u0641\u06cc\u0635\u062f \u067e\u0648\u0627\u0626\u0646\u0679\u0633 \u06c1\u06d2\u06d4 task_completed<\/code>\u06c1\u0645 \u0646\u06d2 \u062d\u0635\u06c1 \u0644\u06cc\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0637\u0628\u0642\u0627\u062a \u0645\u06cc\u06ba \u0679\u0627\u0626\u0631\u0688 \u062a\u0628\u062f\u06cc\u0644\u06cc\u0648\u06ba \u06a9\u0627 \u0627\u0637\u0644\u0627\u0642 \u06a9\u06cc\u0627\u06d4 \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u0645\u06cc\u06ba \u062a\u0645\u0627\u0645 \u0646\u0645\u0628\u0631 \u0627\u0633\u06cc \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u0633\u06d2 \u0622\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
    \u0627\u0633 \u0645\u0636\u0645\u0648\u0646 \u06a9\u0627 \u062a\u0645\u0627\u0645 \u06a9\u0648\u0688 \u062f\u0631\u062c \u0630\u06cc\u0644 \u0633\u0627\u062a\u06be\u06cc \u0646\u0648\u0679 \u0628\u06a9 \u0645\u06cc\u06ba \u0622\u062e\u0631 \u0633\u06d2 \u0622\u062e\u0631 \u062a\u06a9 \u0686\u0644\u062a\u0627 \u06c1\u06d2: 08_uplift_modeling\/uplift_demo.ipynb<\/code>. \u0630\u062e\u06cc\u0631\u06c1 \u06a9\u0644\u0648\u0646 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633\u06d2 \u0686\u0644\u0627\u0626\u06cc\u06ba\u06d4 uplift_demo.py<\/code> \u06c1\u0645 \u062a\u0645\u0627\u0645 \u0646\u062a\u0627\u0626\u062c \u06a9\u0648 \u062f\u0648\u0628\u0627\u0631\u06c1 \u067e\u06cc\u0634 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
    \u0648\u0631\u06a9\u0646\u06af \u0645\u062b\u0627\u0644 \u0633\u06cc\u0679 \u0627\u067e<\/h2>\n
    \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u0635\u0627\u0631\u0641 \u06a9\u06cc \u0637\u0631\u0641 \u0633\u06d2 \u0679\u0648\u06af\u0644 \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u0645\u0646\u062a\u062e\u0628 \u06a9\u0631\u062f\u06c1 AI \u0633\u0645\u0631\u06cc \u0641\u06cc\u0686\u0631\u0632 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 SaaS \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u06cc \u0646\u0642\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 50,000 \u0635\u0627\u0631\u0641\u06cc\u0646\u060c opt_in_agent_mode<\/code> \u067e\u0631\u0648\u0633\u06cc\u0633\u0646\u06af \u06a9\u0627\u0644\u0645 \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 task_completed<\/code> \u0628\u0627\u0626\u0646\u0631\u06cc \u0646\u062a\u06cc\u062c\u06c1 \u06a9\u06d2 \u0633\u0627\u062a\u06be\u06d4 \u0645\u0634\u063a\u0648\u0644\u06cc\u062a \u06a9\u06d2 \u062f\u0631\u062c\u0627\u062a (\u06a9\u0645\u060c \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2\u060c \u0627\u0648\u0646\u0686\u06d2) \u06a9\u06cc\u067e\u0686\u0631 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06c1 \u06c1\u0631 \u0635\u0627\u0631\u0641 \u0622\u067e \u06a9\u06d2 \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u062a\u0646\u06cc \u0641\u0639\u0627\u0644 \u0637\u0648\u0631 \u067e\u0631 \u062a\u0639\u0627\u0645\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u0688\u06cc\u0679\u0627 \u0644\u0648\u0688 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0627\u06cc\u06a9 \u0628\u06cc\u0633 \u0644\u0627\u0626\u0646 \u0642\u0627\u0626\u0645 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n
    import pandas as pd\nimport numpy as np\n\ndf = pd.read_csv(\"data\/synthetic_llm_logs.csv\")\nprint(df.shape)\nprint(df[[\"engagement_tier\", \"opt_in_agent_mode\", \"task_completed\"]].head(10))\n\n# Opt-in rates by tier\nprint(\"nOpt-in rate by engagement tier:\")\nprint(df.groupby(\"engagement_tier\").opt_in_agent_mode.mean().round(3))\n\n# Naive ATE: treated minus control\nnaive_ate = (\n    df[df.opt_in_agent_mode == 1].task_completed.mean()\n    - df[df.opt_in_agent_mode == 0].task_completed.mean()\n)\nprint(f\"nNaive ATE (treated - control): {naive_ate:+.4f}\")\nprint(f\"Treated users: {(df.opt_in_agent_mode == 1).sum():,}\")\nprint(f\"Control users: {(df.opt_in_agent_mode == 0).sum():,}\")\n<\/code><\/pre>\n
    \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
    (50000, 16)\n  engagement_tier  opt_in_agent_mode  task_completed\n0          medium                  0               0\n...\n\nOpt-in rate by engagement tier:\nengagement_tier\nheavy     0.647\nlight     0.120\nmedium    0.353\nName: opt_in_agent_mode, dtype: float64\n\nNaive ATE (treated - control): +0.2106\nTreated users: 13,451\nControl users: 36,549\n<\/code><\/pre>\n
    \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> 50,000 \u0642\u0637\u0627\u0631\u06cc\u06ba \u0644\u0648\u0688 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0622\u067e \u0641\u0648\u0631\u06cc \u0637\u0648\u0631 \u067e\u0631 \u0633\u0646\u062c\u06cc\u062f\u06c1 \u0645\u0635\u0631\u0648\u0641\u06cc\u062a \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06d2 \u0646\u0645\u0648\u0646\u06d2 \u062f\u06cc\u06a9\u06be\u06cc\u06ba \u06af\u06d2\u06d4 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 64.7%\u060c \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2 \u062f\u0631\u062c\u06d2 \u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 35.3%\u060c \u0627\u0648\u0631 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0635\u0631\u0641 12% \u062a\u06be\u06d2\u06d4 \u0628\u0648\u0644\u06cc ATE +0.2106 \u06c1\u06d2\u060c \u062c\u0648 \u0627\u0635\u0644 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0627\u062b\u0631 \u0633\u06d2 \u062f\u0648\u06af\u0646\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u06cc\u06c1 \u0641\u0631\u0642 \u0627\u0646\u062a\u062e\u0627\u0628\u06cc \u062a\u0639\u0635\u0628 \u06a9\u06cc \u0639\u06a9\u0627\u0633\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0633 \u06a9\u0627 \u0645\u0637\u0644\u0628 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0639\u0644\u0627\u062c \u0634\u062f\u06c1 \u06af\u0631\u0648\u067e \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc \u0637\u0631\u0641 \u0645\u062a\u0648\u062c\u06c1 \u06c1\u06d2 \u062c\u0648 \u0641\u0639\u0627\u0644\u06cc\u062a \u0633\u06d2 \u0642\u0637\u0639 \u0646\u0638\u0631 \u0645\u0632\u06cc\u062f \u06a9\u0627\u0645 \u0645\u06a9\u0645\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 +0.21 \u0646\u0645\u0628\u0631 \u0641\u0646\u06a9\u0634\u0646\u0644 \u0627\u062b\u0631 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0645\u0635\u0631\u0648\u0641\u06cc\u062a \u06a9\u06cc \u0633\u0637\u062d\u0648\u06ba \u06a9\u06cc \u067e\u06cc\u0645\u0627\u0626\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u0627\u0628 \u0628\u0648\u0644\u06cc \u0637\u0628\u0642\u0627\u062a\u06cc \u0641\u0631\u0642 \u067e\u0631 \u0627\u06cc\u06a9 \u0646\u0638\u0631 \u0688\u0627\u0644\u06cc\u06ba\u060c \u062c\u0633 \u0633\u06d2 \u0638\u0627\u06c1\u0631 \u06c1\u0648\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0645 \u062c\u0633 \u0645\u062a\u0641\u0627\u0648\u062a \u06a9\u0627 \u0635\u062d\u06cc\u062d \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06cc \u06a9\u0648\u0634\u0634 \u06a9\u0631 \u0631\u06c1\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
    # Naive per-tier gap (confounded but directionally useful)\nprint(\"Naive per-tier treated vs. control completion rate:\")\nfor tier in [\"light\", \"medium\", \"heavy\"]:\n    sub = df[df.engagement_tier == tier]\n    t_rate = sub[sub.opt_in_agent_mode == 1].task_completed.mean()\n    c_rate = sub[sub.opt_in_agent_mode == 0].task_completed.mean()\n    print(f\"  {tier:8s}: treated={t_rate:.3f}, control={c_rate:.3f}, \"\n          f\"diff={t_rate - c_rate:+.3f}\")\n<\/code><\/pre>\n
    \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
    Naive per-tier treated vs. control completion rate:\n  light   : treated=0.551, control=0.455, diff=+0.096\n  medium  : treated=0.745, control=0.670, diff=+0.075\n  heavy   : treated=0.891, control=0.824, diff=+0.067\n<\/code><\/pre>\n
    \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u06cc\u06c1\u0627\u06ba \u062a\u06a9 \u06a9\u06c1 \u062e\u0627\u0645 \u062e\u0644\u0644 \u06a9\u06d2 \u0648\u0642\u0641\u06d2 \u062f\u0631\u062c \u0630\u06cc\u0644 \u062a\u0631\u062a\u06cc\u0628 \u0645\u06cc\u06ba \u067e\u06cc\u0634 \u06a9\u06cc\u06d2 \u062c\u0627\u062a\u06d2 \u06c1\u06cc\u06ba: \u06c1\u0644\u06a9\u0627 > \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2 > \u0628\u06be\u0627\u0631\u06cc (+0.096 > +0.075 > +0.067)\u06d4 \u06c1\u0644\u06a9\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0646\u0646\u062f\u06af\u0627\u0646 \u06a9\u06d2 \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u0645\u06cc\u06ba \u0633\u0628 \u0633\u06d2 \u0628\u0691\u0627 \u0641\u0631\u0642 \u06c1\u06d2\u060c \u062c\u0628\u06a9\u06c1 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u067e\u0627\u0633 \u0633\u0628 \u0633\u06d2 \u0686\u06be\u0648\u0679\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u06cc\u06c1 \u0645\u062a\u0636\u0627\u062f \u06c1\u06d2\u060c \u06cc\u06c1 \u0641\u0631\u0636 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u06a9\u06c1 \u0628\u062c\u0644\u06cc \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u06c1\u0645\u06cc\u0634\u06c1 \u0633\u0628 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0641\u0627\u0626\u062f\u06c1 \u0627\u0679\u06be\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0644\u06cc\u06a9\u0646 \u06cc\u06c1 AI \u062e\u0644\u0627\u0635\u06c1 \u06a9\u06cc \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u0639\u0646\u06cc \u062e\u06cc\u0632 \u06c1\u06d2\u06d4 \u06c1\u0644\u06a9\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0627\u06a9\u062b\u0631 \u0644\u0645\u0628\u06d2 \u062f\u06be\u0627\u06af\u0648\u06ba \u0645\u06cc\u06ba \u0633\u06cc\u0627\u0642 \u0648 \u0633\u0628\u0627\u0642 \u06a9\u06be\u0648 \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0633\u0628 \u0633\u06d2 \u0627\u0648\u067e\u0631 \u06a9\u06d2 \u062e\u0644\u0627\u0635\u06d2 \u0633\u06d2 \u062d\u0642\u06cc\u0642\u06cc \u0637\u0648\u0631 \u067e\u0631 \u0641\u0627\u0626\u062f\u06c1 \u0627\u0679\u06be\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u0646\u06d2 \u067e\u06c1\u0644\u06d2 \u0633\u06d2 \u06c1\u06cc \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u0637\u0648\u0631 \u067e\u0631 \u062a\u06cc\u0627\u0631 \u06a9\u06cc\u0627 \u06c1\u06d2 \u06a9\u06c1 \u06a9\u0633 \u0637\u0631\u062d \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u0648 \u0646\u06cc\u0648\u06cc\u06af\u06cc\u0679 \u06a9\u06cc\u0627 \u062c\u0627\u0626\u06d2 \u0627\u0648\u0631 \u062e\u0644\u0627\u0635\u06d2 \u06a9\u0648 \u0645\u062f\u062f\u06af\u0627\u0631 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u062f\u062e\u0644 \u0627\u0646\u062f\u0627\u0632\u06cc \u0645\u0639\u0644\u0648\u0645 \u06c1\u0648\u06d4 \u0679\u06cc \u0644\u0631\u0646\u0631 \u06a9\u0627 \u0627\u06af\u0644\u0627 \u0645\u0631\u062d\u0644\u06c1 \u06c1\u0631 \u067e\u0631\u062a \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u0633\u0648\u0627\u0644 \u06a9\u06d2 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0648 \u06a9\u0646\u0679\u0631\u0648\u0644 \u06a9\u0631\u06a9\u06d2 \u0627\u0646 \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u06a9\u0648 \u0645\u0632\u06cc\u062f \u0628\u06c1\u062a\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u0634\u06a9\u0644 1: \u0645\u062a\u0641\u0627\u0648\u062a \u0639\u0644\u0627\u062c \u06a9\u06d2 \u0627\u062b\u0631\u0627\u062a \u06a9\u06cc \u062a\u0635\u0648\u0631\u0627\u062a\u06cc \u0645\u062b\u0627\u0644\u06d4 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0627\u0648\u0631 \u0679\u0631\u06cc\u0679\u0688 \u0688\u0633\u0679\u0631\u06cc \u0628\u06cc\u0648\u0634\u0646\u0632 (\u0688\u0627\u0679\u0688 \u0627\u0648\u0631 \u0679\u06be\u0648\u0633 \u0644\u0627\u0626\u0646\u0632) \u06c1\u0631 \u0634\u0631\u06a9\u062a \u06a9\u06d2 \u0627\u0633\u0679\u0631\u06cc\u0679\u0645 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062f\u06a9\u06be\u0627\u0626\u06d2 \u06af\u0626\u06d2 \u06c1\u06cc\u06ba\u06d4 CATE (\u062f\u0648 \u0645\u0646\u062d\u0646\u06cc \u062e\u0637\u0648\u0637 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u0641\u0631\u0642) \u0641\u06cc \u0679\u0627\u0626\u0631 \u06c1\u0644\u06a9\u06d2 \u0633\u06d2 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u062a\u06a9 \u06a9\u0645 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u0646\u06cc\u0686\u06d2 \u0648\u0627\u0644\u0627 \u067e\u06cc\u0646\u0644 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u06a9\u0633 \u0637\u0631\u062d ATE \u0627\u0633 \u067e\u06be\u06cc\u0644\u0627\u0624 \u06a9\u0648 \u0627\u06cc\u06a9 \u06c1\u06cc \u0627\u0648\u0633\u0637 \u0645\u06cc\u06ba \u0633\u0645\u06cc\u0679\u062a\u0627 \u06c1\u06d2\u060c \u06cc\u06c1 \u063a\u0644\u0637 \u0628\u06cc\u0627\u0646\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0641\u06cc\u0686\u0631 \u0627\u0635\u0644 \u0645\u06cc\u06ba \u06c1\u0631 \u0637\u0628\u0642\u06c1 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0633 \u0637\u0631\u062d \u0628\u0631\u062a\u0627\u0624 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/em><\/p>\n
    \u0679\u06cc \u0644\u0631\u0646\u0631 \u062f\u0648 \u0645\u06a9\u0645\u0644 \u0637\u0648\u0631 \u067e\u0631 \u0627\u0644\u06af \u0627\u0644\u06af \u0645\u0627\u0688\u0644\u0632 \u0645\u06cc\u06ba \u0641\u0679 \u0628\u06cc\u0679\u06be\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06cc\u06a9 \u0639\u0644\u0627\u062c \u06af\u0631\u0648\u067e \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0648\u0631 \u0627\u06cc\u06a9 \u06a9\u0646\u0679\u0631\u0648\u0644 \u06af\u0631\u0648\u067e \u06a9\u06d2 \u0644\u06cc\u06d2\u06d4 \u06a9\u0633\u06cc \u0628\u06be\u06cc \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u067e\u0631\u0648\u0633\u06cc\u0633 \u0634\u062f\u06c1 \u0645\u0627\u0688\u0644 \u06a9\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u0627\u0648\u0631 \u0627\u0633 \u0635\u0627\u0631\u0641 \u06a9\u06cc \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0645\u0627\u0688\u0644 \u06a9\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u0641\u0631\u0642 \u06c1\u06d2\u06d4<\/p>\n
    from sklearn.linear_model import LinearRegression\nimport pandas as pd\nimport numpy as np\n\n# Build feature matrix: query_confidence + engagement_tier dummies\nX_full = pd.get_dummies(\n    df[[\"query_confidence\", \"engagement_tier\"]],\n    drop_first=False\n).astype(float)\n\nfeature_cols = X_full.columns.tolist()\nprint(\"Feature columns:\", feature_cols)\n\nX_all = X_full.values\ntreated_mask = df.opt_in_agent_mode == 1\ncontrol_mask = ~treated_mask\n\nX1 = X_all[treated_mask]    # features for treated users\nY1 = df[treated_mask].task_completed.values\nX0 = X_all[control_mask]    # features for control users\nY0 = df[control_mask].task_completed.values\n\n# Fit separate models on each arm\nm1 = LinearRegression().fit(X1, Y1)   # outcome model for treated\nm0 = LinearRegression().fit(X0, Y0)   # outcome model for control\n\n# CATE = mu_1(x) - mu_0(x)\ncate_t = m1.predict(X_all) - m0.predict(X_all)\ndf[\"cate_tlearner\"] = cate_t\n\nprint(f\"nMean CATE (T-learner): {cate_t.mean():+.4f}\")\nprint(\"nMean predicted CATE by engagement tier:\")\nprint(df.groupby(\"engagement_tier\").cate_tlearner.mean().round(4))\n<\/code><\/pre>\n
    \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
    Feature columns: ['query_confidence', 'engagement_tier_heavy', 'engagement_tier_light', 'engagement_tier_medium']\n\nMean CATE (T-learner): +0.0847\n\nMean predicted CATE by engagement tier:\nengagement_tier\nheavy     0.0665\nlight     0.0954\nmedium    0.0744\nName: cate_tlearner, dtype: float64\n<\/code><\/pre>\n
    \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u0634\u0631\u06a9\u062a \u06a9\u06cc \u062a\u06c1\u0648\u06ba \u06a9\u0648 \u0627\u06cc\u06a9 \u06af\u0631\u0645 \u06a9\u0627\u0644\u0645 \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u0627\u0646\u06a9\u0648\u0688 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0645\u0633\u062a\u0642\u0644 \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0633\u062a\u0641\u0633\u0627\u0631 \u06a9\u06d2 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0648 \u0628\u0631\u0642\u0631\u0627\u0631 \u0631\u06a9\u06be\u06cc\u06ba\u06d4 \u062f\u0648 LinearRegression<\/code> \u0645\u0627\u0688\u0644 \u0627\u0644\u06af \u0633\u06d2 \u0641\u0679 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba: m1<\/code> \u0645\u0646\u062a\u062e\u0628 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u06a9\u0627\u0645 \u06a9\u06cc \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u0634\u0631\u0648\u0637 \u062a\u0648\u0642\u0639\u0627\u062a \u0633\u06cc\u06a9\u06be\u06cc\u06ba\u06d4 m0<\/code> \u0627\u06cc\u06a9 \u06c1\u06cc \u0686\u06cc\u0632 \u063a\u06cc\u0631 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0646\u0646\u062f\u06af\u0627\u0646 \u0645\u06cc\u06ba \u0633\u06cc\u06a9\u06be\u06cc \u062c\u0627\u062a\u06cc \u06c1\u06d2\u06d4 \u0641\u06cc\u0686\u0631 \u0648\u0627\u0644\u06d2 \u062a\u0645\u0627\u0645 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 x<\/code>\u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u06c1\u06d2: m1(x) - m0(x)<\/code>.<\/p>\n
    \u0622\u0624\u0679 \u067e\u0679 \u0628\u0648\u0644\u06cc \u0641\u0631\u0642 \u06a9\u06cc \u0633\u0645\u062a \u06a9\u06cc \u062a\u0635\u062f\u06cc\u0642 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06a9\u0648 \u062a\u06cc\u0632 \u062a\u0631 \u0628\u0646\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u062a\u0645\u0627\u0645 50,000 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0648\u0633\u0637 CATE +0.0847 \u06c1\u06d2\u060c \u062c\u0648 \u06a9\u06c1 +0.08 \u06a9\u06cc \u0627\u0635\u0644 \u0642\u06cc\u0645\u062a \u06a9\u06d2 \u0642\u0631\u06cc\u0628 \u06c1\u06d2\u06d4 \u062f\u0631\u062c\u0627\u062a \u06a9\u06cc \u062a\u0631\u062a\u06cc\u0628 \u06c1\u0644\u06a9\u06cc (+0.0954)> \u062f\u0631\u0645\u06cc\u0627\u0646\u06cc (+0.0744)> \u0628\u06be\u0627\u0631\u06cc (+0.0665) \u06c1\u06d2\u06d4 +0.2106 Naive ATE \u06c1\u0644\u06a9\u06d2 \u0627\u0648\u0631 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 1.4x \u0641\u0631\u0642 \u06a9\u0648 \u0686\u06be\u067e\u0627 \u0631\u06c1\u0627 \u062a\u06be\u0627\u06d4 \u06cc\u06c1 \u067e\u06be\u06cc\u0644\u0627\u0624 \u0627\u06cc\u06a9 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0627 \u0627\u0634\u0627\u0631\u06c1 \u06c1\u06d2\u06d4<\/p>\n
    \u0679\u06cc \u0644\u0631\u0646\u0631\u0632 \u06a9\u06d2 \u067e\u0627\u0633 \u0646\u0627\u0645 \u0631\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0642\u0627\u0628\u0644 \u0627\u06cc\u06a9 \u0627\u06c1\u0645 \u0627\u0646\u062a\u0628\u0627\u06c1 \u06c1\u06d2\u06d4 \u06cc\u0639\u0646\u06cc\u060c \u0627\u06af\u0631 \u0627\u06cc\u06a9 \u0628\u0627\u0632\u0648 \u062f\u0648\u0633\u0631\u06d2 \u0633\u06d2 \u0628\u06c1\u062a \u0686\u06be\u0648\u0679\u0627 \u06c1\u06d2 (\u06cc\u06c1\u0627\u06ba: 13,451 \u0679\u0631\u06cc\u0679\u0688 \u0628\u0645\u0642\u0627\u0628\u0644\u06c1 36,549 \u06a9\u0646\u0679\u0631\u0648\u0644)\u060c \u0686\u06be\u0648\u0679\u06d2 \u0628\u0627\u0632\u0648 \u067e\u0631 \u062a\u0631\u0628\u06cc\u062a \u06cc\u0627\u0641\u062a\u06c1 \u0645\u0627\u0688\u0644 \u0632\u06cc\u0627\u062f\u06c1 \u0641\u0631\u0642 \u062f\u06a9\u06be\u0627 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06af\u0631 \u0622\u067e \u06a9\u06d2 \u067e\u0627\u0633 \u06a9\u0644 50,000 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06c1\u06cc\u06ba \u062a\u0648 \u0644\u06a9\u06cc\u0631\u06cc \u0631\u06cc\u06af\u0631\u06cc\u0634\u0646 \u0627\u0633 \u06a9\u0648 \u0627\u0686\u06be\u06cc \u0637\u0631\u062d \u0633\u06d2 \u06c1\u06cc\u0646\u0688\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06af\u0644\u06d2 \u0645\u0631\u062d\u0644\u06d2 \u0645\u06cc\u06ba \u0627\u06cc\u06a9\u0633 \u0644\u0631\u0646\u0631 \u0628\u0631\u0627\u06c1 \u0631\u0627\u0633\u062a \u0639\u062f\u0645 \u062a\u0648\u0627\u0632\u0646 \u06a9\u0648 \u062f\u0648\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
    \u0645\u0631\u062d\u0644\u06c1 2: \u0627\u06cc\u06a9\u0633 \u0644\u0631\u0646\u0631 (\u063a\u06cc\u0631 \u0645\u062a\u0648\u0627\u0632\u0646 \u0639\u0644\u0627\u062c \u0622\u0631\u0645 \u067e\u0631\u0648\u0633\u06cc\u0633\u0646\u06af)<\/h2>\n
    \u0686\u06be\u0648\u0679\u06d2 \u0628\u0627\u0632\u0648 \u06a9\u06cc CATE \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u0645\u06cc\u06ba \u0645\u062f\u062f \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 X-learner \u0628\u0691\u06d2 \u0628\u0627\u0632\u0648 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631 \u06a9\u06d2 T-learner \u06a9\u0648 \u0628\u06c1\u062a\u0631 \u0628\u0646\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u06cc\u06c1 \u06a9\u0645\u067e\u06cc\u0648\u0679\u0646\u06af \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u06a9\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0645\u0645\u0646\u0648\u0639\u06c1 \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u062b\u0631<\/em> \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0645 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u0646\u062a\u0627\u0626\u062c \u0633\u06d2 \u06a9\u0631\u0627\u0633 \u067e\u0631\u0641\u0627\u0631\u0645\u0646\u0633 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc\u06d2 \u06af\u0626\u06d2 \u0645\u062a\u0636\u0627\u062f \u0646\u062a\u0627\u0626\u062c \u06a9\u0648 \u0645\u0645\u062a\u0627\u0632 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
    \u0637\u0631\u06cc\u0642\u06c1 \u06a9\u0627\u0631 \u0686\u0627\u0631 \u0645\u0631\u0627\u062d\u0644 \u067e\u0631 \u0645\u0634\u062a\u0645\u0644 \u06c1\u06d2:<\/p>\n
      \n
    1. \n
      \u0646\u062a\u06cc\u062c\u06c1 \u062e\u06cc\u0632 \u0645\u0627\u0688\u0644 \u0641\u0679 m0<\/code> \u0627\u0648\u0631 m1<\/code> \u06c1\u0631 \u0628\u0627\u0632\u0648 \u067e\u0631 (\u0679\u06cc \u0644\u0631\u0646\u0631 \u06a9\u06cc \u0637\u0631\u062d)<\/p>\n<\/li>\n
    2. \n
      \u067e\u0631\u0648\u0633\u06cc\u0633\u0688 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2: \u06a9\u0645\u067e\u06cc\u0648\u0679 D1 = Y1 - m0(X1)<\/code>\u06cc\u06c1 \u0641\u0631\u0642 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0631 \u0639\u0644\u0627\u062c \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641 \u0646\u06d2 \u0627\u0635\u0644 \u0645\u06cc\u06ba \u06a9\u06cc\u0627 \u062d\u0627\u0635\u0644 \u06a9\u06cc\u0627 \u0627\u0648\u0631 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0645\u0627\u0688\u0644 \u0646\u06d2 \u06a9\u06cc\u0627 \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06c1\u06d2 \u06a9\u06c1 \u0648\u06c1 \u0639\u0644\u0627\u062c \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06cc\u06ba \u06af\u06d2\u06d4<\/p>\n<\/li>\n
    3. \n
      \u06a9\u0646\u0679\u0631\u0648\u0644 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2: \u06a9\u0645\u067e\u06cc\u0648\u0679 D0 = m1(X0) - Y0<\/code>\u067e\u0631\u0648\u0633\u06cc\u0633 \u0634\u062f\u06c1 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u0641\u0631\u0642 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0631 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0635\u0627\u0631\u0641 \u067e\u0631\u0648\u0633\u06cc\u0633\u0646\u06af \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u06a9\u06cc\u0627 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06d2 \u06af\u0627 \u0627\u0648\u0631 \u0648\u06c1 \u0627\u0635\u0644 \u0645\u06cc\u06ba \u06a9\u06cc\u0627 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06d2 \u06af\u0627\u06d4<\/p>\n<\/li>\n
    4. \n
      \u06c1\u0645 \u062f\u0648 \u0679\u0627\u0624 \u0631\u06cc\u06af\u0631\u06cc\u0633\u0631 (\u0627\u06cc\u06a9 \u0641\u06cc \u0628\u0627\u0632\u0648) \u06a9\u0648 \u0641\u0679 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u067e\u06be\u0631 \u0648\u0632\u0646 \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u0631\u062c\u062d\u0627\u0646 \u06a9\u06d2 \u0627\u0633\u06a9\u0648\u0631 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0627\u0646 \u06a9\u0648 \u062c\u0648\u0691 \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u067e\u0627\u0631\u0679\u06cc (K\u00fcnzel et al.): tau(x) = g(x) * tau_1(x) + (1 - g(x)) * tau_0(x)<\/code>\u062c\u06c1\u0627\u06ba g(x) \u0631\u062c\u062d\u0627\u0646 \u06a9\u0627 \u0633\u06a9\u0648\u0631 \u06c1\u06d2\u06d4 \u062c\u0628 g(x) \u06a9\u0645 \u06c1\u0648\u062a\u0627 \u06c1\u06d2 (\u0627\u0633 \u0641\u0646\u06a9\u0634\u0646\u0644 \u0627\u06cc\u0631\u06cc\u0627 \u0645\u06cc\u06ba \u0686\u0646\u062f \u0635\u0627\u0631\u0641\u06cc\u0646 \u067e\u0631 \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u06cc \u062c\u0627\u062a\u06cc \u06c1\u06d2)\u060c \u0628\u0691\u06d2 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0633\u06cc\u06a9\u0679\u0631 \u0645\u06cc\u06ba \u0627\u0646\u062f\u0627\u0632\u06d2 \u06a9\u06d2 \u0645\u0637\u0627\u0628\u0642 tau_0 \u06a9\u0648 \u0632\u06cc\u0627\u062f\u06c1 \u0648\u0632\u0646 \u062f\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0639\u0644\u06cc g(x) tau_1 \u06a9\u0648 \u0632\u06cc\u0627\u062f\u06c1 \u0648\u0632\u0646 \u062f\u06cc\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<\/li>\n<\/ol>\n
      from sklearn.linear_model import LinearRegression, LogisticRegression\n\n# Step 1: m0 and m1 already fitted in Step 1 above\n\n# Step 2: imputed treatment effects for treated group\nD1 = Y1 - m0.predict(X1)     # Y(1) - mu_0(X1)\n\n# Step 3: imputed treatment effects for control group\nD0 = m1.predict(X0) - Y0     # mu_1(X0) - Y(0)\n\n# Fit tau regressors on each arm\ntau1_model = LinearRegression().fit(X1, D1)  # tau for treated arm\ntau0_model = LinearRegression().fit(X0, D0)  # tau for control arm\n\n# Step 4: estimate propensity score e(x) = P(T=1 | X)\nps_model = LogisticRegression(max_iter=1000).fit(X_all, df.opt_in_agent_mode.values)\ne_x = ps_model.predict_proba(X_all)[:, 1]\n\n# Kunzel et al. (2019): tau(x) = g(x)*tau_1(x) + (1 - g(x))*tau_0(x)\ntau1_all = tau1_model.predict(X_all)\ntau0_all = tau0_model.predict(X_all)\ncate_x = e_x * tau1_all + (1 - e_x) * tau0_all\ndf[\"cate_xlearner\"] = cate_x\n\nprint(f\"Mean CATE (X-learner): {cate_x.mean():+.4f}\")\nprint(\"nMean predicted CATE by engagement tier:\")\nprint(df.groupby(\"engagement_tier\").cate_xlearner.mean().round(4))\n\n# Compare T-learner vs X-learner\nprint(\"nT-learner vs X-learner per tier:\")\ncomp = df.groupby(\"engagement_tier\")[[\"cate_tlearner\", \"cate_xlearner\"]].mean().round(4)\nprint(comp)\n<\/code><\/pre>\n
      \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
      Mean CATE (X-learner): +0.0847\n\nMean predicted CATE by engagement tier:\nengagement_tier\nheavy     0.0665\nlight     0.0954\nmedium    0.0744\nName: cate_xlearner, dtype: float64\n\nT-learner vs X-learner per tier:\n                 cate_tlearner  cate_xlearner\nengagement_tier\nheavy                   0.0665         0.0665\nlight                   0.0954         0.0954\nmedium                  0.0744         0.0744\n<\/code><\/pre>\n
      \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u0627\u06cc\u06a9 \u0644\u06a9\u06cc\u0631\u06cc \u0646\u062a\u0627\u0626\u062c \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u0627\u0648\u0631 \u0686\u0627\u0631 \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2\u060c \u0679\u06cc \u0644\u0631\u0646\u0631 \u0627\u0648\u0631 \u0627\u06cc\u06a9\u0633 \u0644\u0631\u0646\u0631 \u0627\u06cc\u06a9 \u06c1\u06cc \u067e\u0631\u062a \u06a9\u06d2 \u062d\u0633\u0627\u0628 \u0633\u06d2 CATE \u062a\u06cc\u0627\u0631 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0627\u0633 \u0645\u0639\u0627\u06c1\u062f\u06d2 \u06a9\u06cc \u062a\u0648\u0642\u0639 \u0627\u0633 \u0648\u0642\u062a \u06a9\u06cc \u062c\u0627\u062a\u06cc \u06c1\u06d2 \u062c\u0628 \u0646\u062a\u06cc\u062c\u06d2 \u0645\u06cc\u06ba \u0622\u0646\u06d2 \u0648\u0627\u0644\u0627 \u0645\u0627\u0688\u0644 \u0627\u0686\u06be\u06cc \u0637\u0631\u062d \u0633\u06d2 \u0648\u0627\u0636\u062d \u06c1\u0648\u06d4 \u0627\u06cc\u06a9\u0633 \u0644\u0631\u0646\u0631 \u06a9\u06cc \u0627\u0646\u0679\u0631\u0633\u06cc\u06a9\u0634\u0646 \u0642\u06cc\u0645\u062a \u0645\u06cc\u06ba \u06a9\u0648\u0626\u06cc \u0627\u06cc\u0633\u06cc \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0634\u0627\u0645\u0644 \u0646\u06c1\u06cc\u06ba \u06c1\u0648\u062a\u06cc \u06c1\u06d2 \u062c\u0633\u06d2 \u0644\u06a9\u06cc\u0631\u06cc \u0645\u0627\u0688\u0644 \u067e\u06c1\u0644\u06d2 \u06c1\u06cc \u0628\u0627\u0632\u06cc\u0627\u0641\u062a \u0646\u06c1\u06cc\u06ba \u06a9\u0631\u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
      \u067e\u06cc\u062f\u0627\u0648\u0627\u0631 \u0645\u06cc\u06ba\u060c \u06a9\u06d2 \u0641\u0648\u0627\u0626\u062f<\/p>\n
      \u062c\u0628 \u0628\u06be\u06cc \u06c1\u0645 \u0628\u06cc\u0633 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u0627\u067e \u06af\u0631\u06cc\u0688 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u06c1\u0645 \u062f\u0648\u0646\u0648\u06ba \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u06a9\u0648 \u0686\u0644\u0627\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633 \u06a9\u0648 \u062a\u0631\u062c\u06cc\u062d \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba \u062c\u0648 \u06c1\u0648\u0644\u0688\u0646\u06af \u0633\u06cc\u0679 \u067e\u0631 \u0628\u06c1\u062a\u0631 \u0627\u0646\u0634\u0627\u0646\u06a9\u0646 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u0648\u06d4<\/p>\n
      \u0645\u0631\u062d\u0644\u06c1 3: \u06a9\u0646\u06cc \u0648\u06a9\u0631 \u0627\u0648\u0631 K \u06a9\u0627 \u0627\u0636\u0627\u0641\u06c1<\/h2>\n
      CATE \u0645\u0627\u0688\u0644 \u0635\u0631\u0641 \u0627\u0633 \u0635\u0648\u0631\u062a \u0645\u06cc\u06ba \u0645\u0641\u06cc\u062f \u06c1\u06d2 \u062c\u0628 \u0635\u0627\u0631\u0641 \u06a9\u06cc \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u0627\u0635\u0644 \u0639\u0644\u0627\u062c \u06a9\u06d2 \u062c\u0648\u0627\u0628 \u06a9\u06d2 \u0622\u0631\u0688\u0631 \u0633\u06d2 \u0645\u0645\u0627\u062b\u0644 \u06c1\u0648\u06d4 Qini \u0648\u06a9\u0631 (Radcliffe, 2007) \u06cc\u06c1 \u067e\u0648\u0686\u06be \u06a9\u0631 \u062c\u0627\u0646\u0686\u062a\u0627 \u06c1\u06d2: \u0627\u06af\u0631 \u06c1\u0645 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u0634\u062f\u06c1 CATE (\u0646\u0632\u0648\u0644) \u06a9\u06d2 \u0645\u0637\u0627\u0628\u0642 \u062a\u0631\u062a\u06cc\u0628 \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0635\u0631\u0641 \u0627\u0648\u067e\u0631 k% \u067e\u0631 \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u062a\u0648 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u0644\u0641\u0679 \u06a9\u0627 \u06a9\u062a\u0646\u0627 \u062d\u0635\u06c1 \u062f\u0631\u062d\u0642\u06cc\u0642\u062a \u0628\u0627\u0632\u06cc\u0627\u0641\u062a \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u061f<\/p>\n
      import matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\n# Sort users by predicted CATE descending\ndf_sorted = df.sort_values(\"cate_tlearner\", ascending=False).copy()\nn = len(df_sorted)\n\n# Compute observed uplift at each percentile cutoff\ntop_ks = np.arange(0.01, 1.01, 0.01)\nqini_vals = []\n\nfor k in top_ks:\n    top_n = max(1, int(k * n))\n    sub = df_sorted.iloc[:top_n]\n    treated_sub = sub[sub.opt_in_agent_mode == 1]\n    control_sub  = sub[sub.opt_in_agent_mode == 0]\n    if len(treated_sub) > 0 and len(control_sub) > 0:\n        uplift = (treated_sub.task_completed.mean()\n                  - control_sub.task_completed.mean())\n    else:\n        uplift = np.nan\n    qini_vals.append(uplift)\n\n# Plot\nfig, ax = plt.subplots(figsize=(8, 4.5))\nax.plot(top_ks * 100, qini_vals, linewidth=2, label=\"T-learner Qini\")\nax.axhline(naive_ate, color=\"gray\", linestyle=\"--\",\n           label=f\"Naive ATE = {naive_ate:.4f}\")\nax.set_xlabel(\"Top-k% of users (sorted by predicted CATE)\")\nax.set_ylabel(\"Observed uplift in top-k group\")\nax.set_title(\"Qini curve: T-learner ranking vs. observed uplift\")\nax.legend()\nplt.tight_layout()\nplt.savefig(\"qini_curve.png\", dpi=140)\nprint(\"Saved qini_curve.png\")\n\n# Print values at selected percentiles\nprint(\"nQini values at selected cutoffs:\")\nfor target_k in [10, 20, 30, 50, 70, 100]:\n    idx = target_k - 1\n    print(f\"  Top {target_k:3d}%: observed uplift = {qini_vals[idx]:.4f}\")\n<\/code><\/pre>\n
      \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
      Saved qini_curve.png\n\nQini values at selected cutoffs:\n  Top  10%: observed uplift = 0.0895\n  Top  20%: observed uplift = 0.1018\n  Top  30%: observed uplift = 0.0959\n  Top  50%: observed uplift = 0.0966\n  Top  70%: observed uplift = 0.1454\n  Top 100%: observed uplift = 0.2106\n<\/code><\/pre>\n
      \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u062a\u0645\u0627\u0645 50,000 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 T- Learner \u06a9\u06cc \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc \u0634\u062f\u06c1 CATE (\u0633\u0628 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u067e\u06c1\u0644\u06d2) \u06a9\u06d2 \u0645\u0637\u0627\u0628\u0642 \u062a\u0631\u062a\u06cc\u0628 \u062f\u06cc\u06ba\u06d4 \u06c1\u0631 \u067e\u0631\u0633\u0646\u0679\u0627\u0626\u0644 \u06a9\u0679 \u0622\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0645 \u0627\u0633 \u0630\u06cc\u0644\u06cc \u06af\u0631\u0648\u067e \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u06a9\u0627\u0645 \u06a9\u06cc \u062a\u06a9\u0645\u06cc\u0644 \u0645\u06cc\u06ba \u062e\u0627\u0645 \u0679\u0631\u06cc\u0679\u0645\u0646\u0679 \u0679\u0648 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0641\u0631\u0642 \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0644\u06af\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u0633\u0628 \u0633\u06d2 \u0627\u0648\u067e\u0631 10% \u06af\u0631\u0648\u067e +0.0895 \u06a9\u0627 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u0627\u0636\u0627\u0641\u06c1 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u0679\u0627\u067e 20% \u06af\u0631\u0648\u067e +0.1018 \u06a9\u0627 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u0627\u0636\u0627\u0641\u06c1 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u062f\u0648\u0646\u0648\u06ba +0.2106 \u06a9\u06d2 \u0628\u0648\u0644\u06cc ATE \u0633\u06d2 \u06a9\u0627\u0641\u06cc \u0646\u06cc\u0686\u06d2 \u06c1\u06cc\u06ba\u06d4 \u06cc\u06c1 \u0627\u0646\u062a\u062e\u0627\u0628 \u0633\u06d2 \u067e\u0631\u06cc\u0634\u0627\u0646 \u06c1\u06d2 \u0627\u0648\u0631 \u0639\u0645\u0644\u06cc \u0627\u062b\u0631\u0627\u062a \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0634\u0631\u06a9\u062a \u06a9\u06cc \u0633\u0637\u062d \u06a9\u06cc \u0639\u06a9\u0627\u0633\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
      \u06cc\u06c1\u0627\u06ba \u06a9\u06cc \u06a9\u0646\u06cc \u0648\u06cc\u0644\u06cc\u0648 \u0628\u06be\u06cc CATE \u0633\u06af\u0646\u0644 \u06a9\u0648 \u0628\u0642\u0627\u06cc\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06d2 \u062a\u0639\u0635\u0628 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0645\u0644\u0627 \u062f\u06cc\u062a\u06cc \u06c1\u06d2\u06d4 \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u06a9\u06d2 \u0645\u0637\u0627\u0628\u0642 \u0633\u0628 \u0633\u06d2 \u0627\u0648\u067e\u0631 54% \u0645\u06cc\u06ba \u062a\u0645\u0627\u0645 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06c1\u0644\u06a9\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0646\u0646\u062f\u06c1 \u06c1\u06cc\u06ba (12% \u06a9\u06cc \u0633\u0628 \u0633\u06d2 \u06a9\u0645 \u0622\u067e\u0679 \u0627\u0646 \u0631\u06cc\u0679 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u062f\u0631\u062c\u06d2)\u060c \u0627\u0633 \u0644\u06cc\u06d2 \u0627\u0633 \u06af\u0631\u0648\u067e \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u067e\u0631\u0648\u0633\u06cc\u0633 \u0634\u062f\u06c1 \u0645\u0627\u0626\u0646\u0633 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0645\u0648\u0627\u0632\u0646\u06c1 \u0627\u0628 \u0628\u06be\u06cc \u0627\u0646\u062f\u0631\u0648\u0646 \u0633\u0637\u062d \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06d2 \u062a\u0639\u0635\u0628 \u06a9\u06cc \u0648\u062c\u06c1 \u0633\u06d2 \u067e\u0631\u06cc\u0634\u0627\u0646 \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u0627\u0648\u067e\u0631 70% (+0.1454) \u0645\u06cc\u06ba \u0686\u06be\u0644\u0627\u0646\u06af \u0627\u0633 \u0645\u0628\u06c1\u0645 \u0627\u062b\u0631 \u06a9\u0648 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u062c\u0628 \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2 \u0627\u0648\u0631 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u06a9\u06d2 \u06af\u0631\u0648\u067e \u0645\u06cc\u06ba \u062f\u0627\u062e\u0644 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0639\u0644\u0627\u062c \u0634\u062f\u06c1 \u0633\u0627\u0626\u06cc\u0688 \u0645\u06cc\u06ba \u0627\u0686\u0627\u0646\u06a9 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba \u062c\u0646 \u06a9\u06cc \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u06cc \u0634\u0631\u062d \u0632\u06cc\u0627\u062f\u06c1 \u06c1\u0648\u062a\u06cc \u06c1\u06d2 (64.7% \u0622\u067e\u0679 \u0627\u0646)\u060c \u062c\u0628\u06a9\u06c1 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0633\u0627\u0626\u06cc\u0688 \u067e\u0631 \u06a9\u0645 \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u06cc \u0634\u0631\u062d \u0648\u0627\u0644\u06d2 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0627 \u063a\u0644\u0628\u06c1 \u0631\u06c1\u062a\u0627 \u06c1\u06d2\u06d4 \u0648\u06c1 \u0633\u067e\u0627\u0626\u06cc\u06a9 \u0633\u0644\u06cc\u06a9\u0634\u0646 \u062a\u0639\u0635\u0628 \u06c1\u06d2\u060c \u0627\u0633 \u06a9\u06d2 \u067e\u06cc\u0686\u06be\u06d2 \u06a9\u0648\u0626\u06cc \u062d\u0642\u06cc\u0642\u06cc CATE \u0633\u06af\u0646\u0644 \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u06d4<\/p>\n
      \u0645\u0634\u0627\u06c1\u062f\u06d2 \u0645\u06cc\u06ba \u0627\u0636\u0627\u0641\u06c1 \u0634\u062f\u06c1 \u062a\u0631\u062a\u06cc\u0628 \u0645\u06cc\u06ba\u060c Qini \u06a9\u0627 \u0642\u0627\u0628\u0644 \u0639\u0645\u0644 \u062e\u0637\u06c1 \u062a\u0642\u0631\u06cc\u0628\u0627\u064b 20% \u0633\u06d2 50% \u062a\u06a9 \u0633\u0631\u0641\u06c1\u0631\u0633\u062a \u06c1\u06d2\u060c \u062c\u0633 \u0645\u06cc\u06ba \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u0632\u06cc\u0627\u062f\u06c1 \u0648\u0627\u0636\u062d \u0637\u0648\u0631 \u067e\u0631 \u0645\u0627\u0688\u0644 \u06a9\u06d2 CATE \u062a\u062e\u0645\u06cc\u0646\u06d2 \u06a9\u0648 \u0627\u0639\u0644\u06cc\u0670 \u0635\u062f\u0648\u0631 \u0633\u06d2 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u060c \u062c\u06c1\u0627\u06ba \u0646\u062a\u0627\u0626\u062c \u06a9\u06cc \u0633\u0637\u062d \u0627\u0648\u0631 \u0631\u062c\u062d\u0627\u0646 \u06a9\u06d2 \u0627\u0633\u06a9\u0648\u0631 \u06a9\u06d2 \u0627\u0631\u062a\u0628\u0627\u0637 \u06a9\u0627 \u063a\u0644\u0628\u06c1 \u06c1\u06d2\u06d4<\/p>\n
      \u0645\u0631\u062d\u0644\u06c1 4: \u0631\u0648\u0644 \u0622\u0624\u0679 \u0631\u0648\u0644\u0632 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0631\u06cc\u06ba\u06d4<\/h2>\n
      CATE \u0645\u0627\u0688\u0644 \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u0634\u062f\u06c1 \u0639\u0644\u0627\u062c \u06a9\u0627 \u0627\u062b\u0631 \u062a\u0641\u0648\u06cc\u0636 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u062d\u062f \u0645\u0642\u0631\u0631 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0646\u06c1\u06cc\u06ba \u062a\u0639\u06cc\u0646\u0627\u062a\u06cc \u06a9\u06cc \u067e\u0627\u0644\u06cc\u0633\u06cc\u0648\u06ba \u0645\u06cc\u06ba \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0631\u06cc\u06ba\u06d4 \u0627\u0646 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u0648 \u0642\u0627\u0628\u0644 \u0628\u0646\u0627\u062a\u0627 \u06c1\u06d2 \u062c\u0646 \u06a9\u06cc \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u0627\u06cc\u06a9 \u062e\u0627\u0635 \u0642\u062f\u0631 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u06c1\u06d2 \u0627\u0648\u0631 \u0627\u0646\u06c1\u06cc\u06ba \u062f\u0648\u0633\u0631\u06d2 \u062a\u0645\u0627\u0645 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062f\u0628\u0627 \u062f\u06cc\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n
      # Inspect the CATE distribution first\nprint(\"CATE distribution (T-learner):\")\nprint(pd.Series(df.cate_tlearner).describe().round(4))\nprint()\n\n# Plot CATE distribution\nfig, ax = plt.subplots(figsize=(8, 4))\nax.hist(df.cate_tlearner, bins=50, edgecolor=\"white\", linewidth=0.5)\nax.axvline(0.085, color=\"red\", linestyle=\"--\", label=\"Threshold = 0.085\")\nax.axvline(df.cate_tlearner.mean(), color=\"gray\", linestyle=\":\",\n           label=f\"Mean CATE = {df.cate_tlearner.mean():.4f}\")\nax.set_xlabel(\"Predicted CATE (T-learner)\")\nax.set_ylabel(\"Number of users\")\nax.set_title(\"Distribution of predicted CATEs\")\nax.legend()\nplt.tight_layout()\nplt.savefig(\"cate_distribution.png\", dpi=140)\nprint(\"Saved cate_distribution.png\")\n\n# Apply rollout rule\nthreshold = 0.085\nselected = df[df.cate_tlearner >= threshold].copy()\nsuppressed = df[df.cate_tlearner < threshold].copy()\n\nprint(f\"nRollout threshold: CATE >= {threshold}\")\nprint(f\"Users selected for rollout: {len(selected):,} ({100*len(selected)\/len(df):.0f}%)\")\nprint(f\"Users suppressed:           {len(suppressed):,} ({100*len(suppressed)\/len(df):.0f}%)\")\nprint()\nprint(\"Tier composition of selected group:\")\nprint((selected.groupby(\"engagement_tier\").size() \/ len(selected)).round(3))\nprint()\nprint(f\"Mean predicted CATE (selected):   {selected.cate_tlearner.mean():.4f}\")\nprint(f\"Mean predicted CATE (suppressed): {suppressed.cate_tlearner.mean():.4f}\")\n<\/code><\/pre>\n
      \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
      CATE distribution (T-learner):\ncount    50000.0000\nmean         0.0847\nstd          0.0126\nmin          0.0515\n25%          0.0731\n50%          0.0897\n75%          0.0963\nmax          0.1021\nName: cate_tlearner, dtype: float64\n\nSaved cate_distribution.png\n\nRollout threshold: CATE >= 0.085\nUsers selected for rollout: 27,203 (54%)\nUsers suppressed:           22,797 (46%)\n\nTier composition of selected group:\nengagement_tier\nlight    1.0\ndtype: float64\n\nMean predicted CATE (selected):   0.0955\nMean predicted CATE (suppressed): 0.0719\n<\/code><\/pre>\n
      \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u062d\u062f \u0645\u0642\u0631\u0631 \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 CATE \u06a9\u06cc \u067e\u0648\u0631\u06cc \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0627 \u062c\u0627\u0626\u0632\u06c1 \u0644\u06cc\u06ba\u06d4 \u062a\u0645\u0627\u0645 50,000 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0645\u06cc\u06ba \u0627\u0648\u0633\u0637 CATE +0.0847 \u06c1\u06d2 \u062c\u0633 \u06a9\u06d2 \u0645\u0639\u06cc\u0627\u0631\u06cc \u0627\u0646\u062d\u0631\u0627\u0641 +0.0126 \u06c1\u06d2\u06d4 \u062d\u062f \u06a9\u0648 +0.085 (\u0627\u0648\u0633\u0637 +0.0847 \u0633\u06d2 \u0628\u0627\u0644\u06a9\u0644 \u0627\u0648\u067e\u0631) \u067e\u0631 \u0633\u06cc\u0679 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0646\u062a\u06cc\u062c\u06d2 \u0645\u06cc\u06ba 27,203 \u0635\u0627\u0631\u0641\u06cc\u0646 (54%) \u06a9\u0648 \u0645\u0646\u062a\u062e\u0628 \u06a9\u06cc\u0627 \u062c\u0627\u0626\u06d2 \u06af\u0627\u06d4<\/p>\n
      \u0645\u0646\u062a\u062e\u0628 \u06af\u0631\u0648\u067e \u06a9\u0627 \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc 100% \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06c1\u06cc\u06ba\u06d4 \u0644\u06a9\u06cc\u0631\u06cc \u0645\u0627\u0688\u0644 \u0627\u0648\u0631 \u0627\u0646 \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u0627\u0633 \u0628\u0627\u062a \u06a9\u0648 \u06cc\u0642\u06cc\u0646\u06cc \u0628\u0646\u0627\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0631 \u067e\u0631\u062a \u0645\u06cc\u06ba CATE \u0631\u06cc\u0646\u062c\u0632 \u062f\u06c1\u0644\u06cc\u0632 \u067e\u0631 \u0645\u062a\u062c\u0627\u0648\u0632 \u0646\u06c1 \u06c1\u0648\u06ba\u06d4 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0646\u06d2 +0.0807 \u0627\u0648\u0631 +0.1021 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 CATE \u06a9\u06cc \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc\u06d4 \u0627\u0648\u0633\u0637 \u0635\u0627\u0631\u0641 \u0646\u06d2 +0.0592 \u0627\u0648\u0631 +0.0812 \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 CATE \u06a9\u06cc \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc\u06d4 0.085 \u06a9\u06cc \u062d\u062f \u0648\u0627\u0636\u062d \u0637\u0648\u0631 \u067e\u0631 \u062f\u0648\u0646\u0648\u06ba \u06a9\u0648 \u0627\u0644\u06af \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n
      \u0645\u0646\u062a\u062e\u0628 \u06af\u0631\u0648\u067e (+0.0955) \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0648\u0633\u0637 \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc CATE \u062f\u0628\u06d2 \u06c1\u0648\u0626\u06d2 \u06af\u0631\u0648\u067e (+0.0719) \u06a9\u06d2 \u0645\u0642\u0627\u0628\u0644\u06d2 \u0645\u06cc\u06ba 33% \u0632\u06cc\u0627\u062f\u06c1 \u06c1\u06d2\u06d4 \u06cc\u06c1 \u0641\u0648\u06a9\u0633 \u0633\u06cc\u06af\u0645\u0646\u0679\u0688 \u0631\u0648\u0644 \u0622\u0624\u0679 \u06a9\u06cc \u0642\u062f\u0631 \u06c1\u06d2\u06d4 54% \u0635\u0627\u0631\u0641\u06cc\u0646 \u0645\u06cc\u06ba AI \u06a9\u06d2 \u062e\u0644\u0627\u0635\u06d2 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0631\u06cc\u06ba \u062c\u0648 \u0633\u0628 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0641\u0627\u0626\u062f\u06c1 \u0627\u0679\u06be\u0627\u0626\u06cc\u06ba \u06af\u06d2\u060c \u0627\u0646\u06c1\u06cc\u06ba \u0686\u06be\u0648\u0679\u06d2 \u0645\u062a\u0648\u0642\u0639 \u0641\u0648\u0627\u0626\u062f \u06a9\u06d2 \u0633\u0627\u062a\u06be \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2 \u0627\u0648\u0631 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u062a\u06a9 \u0645\u062d\u062f\u0648\u062f \u0631\u06a9\u06be\u06cc\u06ba\u060c \u0627\u0648\u0631 \u062f\u0648\u0646\u0648\u06ba \u06af\u0631\u0648\u067e\u0648\u06ba \u06a9\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u0627 \u0688\u06cc\u0679\u0627 \u0627\u06a9\u0679\u06be\u0627 \u06a9\u0631\u06cc\u06ba \u062a\u0627\u06a9\u06c1 \u0686\u0648\u062a\u06be\u0627\u0626\u06cc \u062a\u06a9 \u062d\u062f \u06a9\u0648 \u0628\u06c1\u062a\u0631 \u0628\u0646\u0627\u06cc\u0627 \u062c\u0627 \u0633\u06a9\u06d2\u06d4<\/p>\n
      \"\u062a\u0635\u0648\u06cc\u0631<\/p>\n
      \u0634\u06a9\u0644 2: 50,000 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0645\u0635\u0646\u0648\u0639\u06cc \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u06a9\u06d2 \u0644\u06cc\u06d2 \u067e\u0631\u062a \u06a9\u06d2 \u0644\u062d\u0627\u0638 \u0633\u06d2 CATE \u06a9\u06cc \u062a\u0642\u0633\u06cc\u0645\u06d4 \u0627\u0648\u067e\u0631\u06cc \u067e\u06cc\u0646\u0644 \u0634\u0631\u06a9\u062a \u06a9\u06d2 \u062f\u0631\u062c\u06d2 \u06a9\u06d2 \u0644\u062d\u0627\u0638 \u0633\u06d2 \u06c1\u0645\u0648\u0627\u0631 KDE \u0645\u0646\u062d\u0646\u06cc \u062e\u0637\u0648\u0637 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 (\u0646\u06cc\u0644\u06d2) \u06a9\u0648 \u0633\u0628 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u067e\u06cc\u0634 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u0644\u0633\u0679\u0631 \u06a9\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u060c \u062c\u0628\u06a9\u06c1 \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 (\u0633\u0628\u0632) \u0633\u0628 \u0633\u06d2 \u06a9\u0645 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u0644\u0633\u0679\u0631 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0646\u06cc\u0686\u06d2 \u0648\u0627\u0644\u0627 \u067e\u06cc\u0646\u0644 \u0633\u0627\u062f\u06c1 ATE (+0.2106) \u06a9\u06d2 \u0633\u0627\u062a\u06be \u062d\u0648\u0627\u0644\u06c1 \u0644\u0627\u0626\u0646 \u0627\u0648\u0631 95% \u0628\u0648\u0679\u0633\u0679\u0631\u06cc\u067e \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u06d2 \u0648\u0642\u0641\u06d2 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0648\u0633\u0637 CATE \u0641\u06cc \u067e\u0631\u062a \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u062a\u06cc\u0646\u0648\u06ba \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u06a9\u06d2 CIs \u0628\u0648\u0644\u06cc ATE \u0633\u06d2 \u0628\u0627\u0644\u06a9\u0644 \u0646\u06cc\u0686\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0633 \u0628\u0627\u062a \u06a9\u06cc \u062a\u0635\u062f\u06cc\u0642 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06c1 \u0630\u0631\u0627\u0626\u0639 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06d2 \u062a\u0639\u0635\u0628 \u0633\u06d2 \u067e\u0631\u06cc\u0634\u0627\u0646 \u06c1\u06cc\u06ba\u06d4<\/em><\/p>\n
      \u0631\u06cc\u0644\u06cc\u0632 \u0631\u0648\u0644\u0632 \u06a9\u0627 \u0646\u0642\u0634\u06c1 \u0628\u0631\u0627\u06c1 \u0631\u0627\u0633\u062a \u0641\u06cc\u0686\u0631 \u0641\u0644\u06cc\u06af \u0633\u0633\u0679\u0645 \u067e\u0631\u06d4<\/p>\n
      # Simulate the rollout decision for a single new user\ndef should_show_feature(query_confidence, engagement_tier, threshold=0.085):\n    \"\"\"Returns True if predicted CATE exceeds the rollout threshold.\"\"\"\n    x = pd.get_dummies(\n        pd.DataFrame([{\"query_confidence\": query_confidence,\n                        \"engagement_tier\": engagement_tier}]),\n        drop_first=False\n    ).reindex(columns=feature_cols, fill_value=0).astype(float).values\n    cate = m1.predict(x)[0] - m0.predict(x)[0]\n    return cate >= threshold, round(cate, 4)\n\nshow, cate = should_show_feature(0.72, \"heavy\")\nprint(f\"Heavy user, conf=0.72:  show feature={show}, CATE={cate}\")\n\nshow, cate = should_show_feature(0.72, \"light\")\nprint(f\"Light user, conf=0.72:  show feature={show}, CATE={cate}\")\n\nshow, cate = should_show_feature(0.45, \"medium\")\nprint(f\"Medium user, conf=0.45: show feature={show}, CATE={cate}\")\n<\/code><\/pre>\n
      \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
      Heavy user, conf=0.72:  show feature=False, CATE=0.0667\nLight user, conf=0.72:  show feature=True, CATE=0.0955\nMedium user, conf=0.45: show feature=False, CATE=0.0681\n<\/code><\/pre>\n
      \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> CATE \u06a9\u06cc\u0644\u06a9\u0648\u0644\u06cc\u0634\u0646 \u06a9\u0648 \u0627\u06cc\u06a9 \u0641\u0646\u06a9\u0634\u0646 \u0645\u06cc\u06ba \u0644\u067e\u06cc\u0679\u06cc\u06ba \u062c\u0648 \u0627\u0633 \u0628\u0627\u062a \u06a9\u06cc \u0639\u06a9\u0627\u0633\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0627\u0635\u0644 \u0641\u06cc\u0686\u0631 \u0641\u0644\u06cc\u06af \u0633\u0631\u0648\u0633 \u062c\u0628 \u062f\u0631\u062e\u0648\u0627\u0633\u062a \u06a9\u06cc \u062c\u0627\u062a\u06cc \u06c1\u06d2 \u062a\u0648 \u06a9\u06cc\u0627 \u0639\u0645\u0644 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u0645\u0639\u062a\u062f\u0644 \u0627\u0633\u062a\u0641\u0633\u0627\u0631 \u06a9\u06cc \u0648\u0634\u0648\u0633\u0646\u06cc\u06cc\u062a\u0627 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0628\u06be\u0627\u0631\u06cc \u0635\u0627\u0631\u0641\u06cc\u0646 \u062d\u0627\u0635\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba: show feature=False<\/code> CATE +0.0667 \u06c1\u06d2\u060c 0.085 \u06a9\u06cc \u062d\u062f \u0633\u06d2 \u0646\u06cc\u0686\u06d2\u06d4 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u0626\u06d2 \u0627\u06cc\u06a9 \u06c1\u06cc \u0633\u0648\u0627\u0644 \u06a9\u06cc \u0648\u0634\u0648\u0633\u0646\u06cc\u06cc\u062a\u0627 \u06c1\u06d2\u06d4 show feature=True<\/code> CATE +0.0955 \u06c1\u06d2\u06d4 \u062f\u0631\u0645\u06cc\u0627\u0646\u06d2\u060c \u06a9\u0645 \u0627\u0639\u062a\u0645\u0627\u062f \u0648\u0627\u0644\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 +0.0681 \u062d\u062f \u0633\u06d2 \u0646\u06cc\u0686\u06d2 \u0622\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u06cc\u06c1 \u0622\u0624\u0679 \u067e\u0679 \u0688\u0648\u0645\u06cc\u0646 \u06a9\u06cc \u06a9\u06c1\u0627\u0646\u06cc \u0633\u06d2 \u0645\u0645\u0627\u062b\u0644 \u06c1\u06cc\u06ba\u06d4 AI \u06a9\u0627 \u062e\u0644\u0627\u0635\u06c1 \u0627\u0646 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc \u0645\u062f\u062f \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0646\u06c1\u06cc\u06ba \u0633\u06cc\u0634\u0646 \u06a9\u06d2 \u062f\u0648\u0631\u0627\u0646 \u0633\u06cc\u0627\u0642 \u0648 \u0633\u0628\u0627\u0642 \u06a9\u0648 \u0628\u0631\u0642\u0631\u0627\u0631 \u0631\u06a9\u06be\u0646\u06d2 \u0645\u06cc\u06ba \u062f\u0634\u0648\u0627\u0631\u06cc \u06c1\u0648\u062a\u06cc \u06c1\u06d2\u060c \u0627\u0648\u0631 \u0627\u0646\u06af\u06cc\u062c\u0645\u0646\u0679 \u0644\u06cc\u0626\u0631 \u0627\u0633 \u0645\u0634\u06a9\u0644 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 \u0637\u0627\u0642\u062a\u0648\u0631 \u067e\u0631\u0627\u06a9\u0633\u06cc \u06c1\u06d2\u06d4<\/p>\n
      \u0645\u0631\u062d\u0644\u06c1 5: \u0628\u0648\u0679\u0633\u0679\u0631\u06cc\u067e \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0627 \u0648\u0642\u0641\u06c1<\/h2>\n
      \u0627\u0648\u067e\u0631 CATE \u06a9\u06d2 \u062a\u062e\u0645\u06cc\u0646\u06d2 \u067e\u0648\u0627\u0626\u0646\u0679 \u062a\u062e\u0645\u06cc\u0646\u06d2 \u06c1\u06cc\u06ba \u062c\u0646 \u0645\u06cc\u06ba \u06a9\u0648\u0626\u06cc \u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u0645\u0642\u062f\u0627\u0631 \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u06d4 \u0627\u0633 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u06a9\u06c1 \u06c1\u0645 \u0627\u0633 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0631\u06cc\u0644\u06cc\u0632 \u06a9\u06d2 \u0627\u0635\u0648\u0644 \u0628\u0646\u0627 \u0633\u06a9\u06cc\u06ba\u060c \u06c1\u0645\u06cc\u06ba \u06cc\u06c1 \u062c\u0627\u0646\u0646\u06d2 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2 \u06a9\u06c1 \u06cc\u06c1 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06c1\u0645\u0627\u0631\u06d2 \u0635\u0627\u0631\u0641 \u06a9\u06cc \u0628\u0646\u06cc\u0627\u062f \u06a9\u06d2 \u0645\u062e\u062a\u0644\u0641 \u0646\u0645\u0648\u0646\u0648\u06ba \u0645\u06cc\u06ba \u06a9\u062a\u0646\u0627 \u0645\u0633\u062a\u062d\u06a9\u0645 \u06c1\u06d2\u06d4<\/p>\n
      def bootstrap_cate_ci(df, X_all, feature_cols, n_reps=500, seed=7):\n    \"\"\"Bootstrap 95% CI for mean CATE overall and per engagement tier.\"\"\"\n    rng = np.random.default_rng(seed)\n    n = len(df)\n    tier_reps = {\"light\": [], \"medium\": [], \"heavy\": []}\n    mean_reps = []\n\n    for _ in range(n_reps):\n        idx = rng.integers(0, n, size=n)\n        df_b = df.iloc[idx].reset_index(drop=True)\n        X_b = X_all[idx]\n        treated_b = df_b.opt_in_agent_mode == 1\n        m1_b = LinearRegression().fit(X_b[treated_b], df_b[treated_b].task_completed.values)\n        m0_b = LinearRegression().fit(X_b[~treated_b], df_b[~treated_b].task_completed.values)\n        cate_b = m1_b.predict(X_b) - m0_b.predict(X_b)\n        df_b[\"cate\"] = cate_b\n        for tier in tier_reps:\n            tier_reps[tier].append(df_b[df_b.engagement_tier == tier].cate.mean())\n        mean_reps.append(cate_b.mean())\n\n    cis = {}\n    for tier, vals in tier_reps.items():\n        arr = np.array(vals)\n        cis[tier] = (float(np.percentile(arr, 2.5)),\n                     float(np.percentile(arr, 97.5)))\n    arr = np.array(mean_reps)\n    cis[\"mean\"] = (float(np.percentile(arr, 2.5)),\n                   float(np.percentile(arr, 97.5)))\n    return cis\n\nprint(\"Running bootstrap (500 replicates, seed=7)...\")\ncis = bootstrap_cate_ci(df, X_all, feature_cols, n_reps=500, seed=7)\nprint(f\"Mean CATE   95% CI: [{cis['mean'][0]:+.4f}, {cis['mean'][1]:+.4f}]\")\nprint(f\"Light tier  95% CI: [{cis['light'][0]:+.4f}, {cis['light'][1]:+.4f}]\")\nprint(f\"Medium tier 95% CI: [{cis['medium'][0]:+.4f}, {cis['medium'][1]:+.4f}]\")\nprint(f\"Heavy tier  95% CI: [{cis['heavy'][0]:+.4f}, {cis['heavy'][1]:+.4f}]\")\n<\/code><\/pre>\n
      \u0645\u062a\u0648\u0642\u0639 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631:<\/strong><\/p>\n
      Running bootstrap (500 replicates, seed=7)...\nMean CATE   95% CI: [+0.0744, +0.0951]\nLight tier  95% CI: [+0.0781, +0.1125]\nMedium tier 95% CI: [+0.0596, +0.0892]\nHeavy tier  95% CI: [+0.0483, +0.0842]\n<\/code><\/pre>\n
      \u0645\u0648\u062c\u0648\u062f\u06c1 \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0686\u06be \u06cc\u0648\u06ba \u06c1\u06d2:<\/strong> \u06c1\u0645 \u067e\u0648\u0631\u06d2 50,000 \u0635\u0627\u0631\u0641 \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u06a9\u0648 500 \u0628\u0627\u0631 \u0645\u062a\u0628\u0627\u062f\u0644 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u062f\u0648\u0628\u0627\u0631\u06c1 \u0646\u0645\u0648\u0646\u06c1 \u0628\u0646\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u06c1\u0631 \u0627\u06cc\u06a9 \u0646\u0645\u0648\u0646\u06d2 \u0645\u06cc\u06ba T-learner \u06a9\u0648 \u0631\u06cc\u0641\u0679 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0648\u0631 \u0628\u0648\u0679\u0633\u0679\u0631\u06cc\u067e \u06a9\u06cc \u062a\u06a9\u0631\u0627\u0631 \u0645\u06cc\u06ba \u0627\u0648\u0633\u0637 CATE \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0644\u06af\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u06d2 2.5\u0648\u06cc\u06ba \u0627\u0648\u0631 97.5\u0648\u06cc\u06ba \u067e\u0631\u0633\u0646\u0679\u0627\u0626\u0644 \u06c1\u0631 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06a9\u06d2 \u0644\u06cc\u06d2 95% \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u06d2 \u0648\u0642\u0641\u06d2 \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u0627\u0646 CIs \u0645\u06cc\u06ba \u0686\u06cc\u06a9 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062a\u06cc\u0646 \u0686\u06cc\u0632\u06cc\u06ba \u06c1\u06cc\u06ba: \u067e\u06c1\u0644\u0627\u060c \u0645\u062c\u0645\u0648\u0639\u06cc \u0627\u0648\u0633\u0637 CI (+0.0744, +0.0951) +0.08 \u06a9\u06cc \u0627\u0635\u0644 \u0642\u062f\u0631 \u06a9\u0648 \u0628\u0631\u06cc\u06a9\u0679 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u0627\u0633 \u0628\u0627\u062a \u06a9\u06cc \u062a\u0635\u062f\u06cc\u0642 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u0648\u0627\u0644\u0627 \u06a9\u0627\u0645 \u06a9\u0631 \u0631\u06c1\u0627 \u06c1\u06d2\u06d4 \u062f\u0648\u0633\u0631\u0627\u060c \u06c1\u0644\u06a9\u06d2 \u062f\u0631\u062c\u06d2 \u06a9\u0627 CI (+0.0781, +0.1125) \u0628\u06be\u0627\u0631\u06cc \u062f\u0631\u062c\u06d2 \u06a9\u06d2 CI (+0.0483, +0.0842) \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0648\u0633\u06cc\u0639 \u06c1\u06d2\u06d4 \u06cc\u06c1 \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0645\u0637\u0627\u0628\u0642\u062a \u0631\u06a9\u06be\u062a\u0627 \u06c1\u06d2 \u062c\u0646 \u06a9\u06d2 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u06cc \u0634\u0631\u062d \u0633\u0628 \u0633\u06d2 \u06a9\u0645 \u06c1\u06d2 (12%) \u0627\u0648\u0631 \u0627\u0633 \u0648\u062c\u06c1 \u0633\u06d2 \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u06a9\u0648 \u062f\u0631\u0633\u062a \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0645 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a \u067e\u0631 \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc\u06d4 \u062a\u06cc\u0633\u0631\u0627\u060c \u067e\u0631\u062a CIs \u06a9\u0648 \u062f\u0645 \u0633\u06d2 \u0645\u06a9\u0645\u0644 \u0637\u0648\u0631 \u067e\u0631 \u0627\u0644\u06af \u0646\u06c1\u06cc\u06ba \u06a9\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u06c1\u0644\u06a9\u06cc \u0646\u0686\u0644\u06cc \u062d\u062f (+0.0781) \u0628\u06be\u0627\u0631\u06cc \u0627\u0648\u067e\u0631\u06cc \u0628\u0627\u0624\u0646\u0688 (+0.0842) \u06a9\u0648 \u0628\u0645\u0634\u06a9\u0644 \u0635\u0627\u0641 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u06cc\u0639\u0646\u06cc \u0622\u0631\u0688\u0631 \u0644\u0627\u0626\u0679 > \u0628\u06be\u0627\u0631\u06cc \u0645\u0633\u062a\u062d\u06a9\u0645 \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u0632\u06cc\u0627\u062f\u06c1 \u0646\u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u062a\u0641\u0631\u06cc\u0642 \u0631\u0648\u0644 \u0622\u0624\u0679 \u06a9\u06d2 \u0628\u0627\u0631\u06d2 \u0645\u06cc\u06ba \u06a9\u0627\u0631\u0648\u0628\u0627\u0631\u06cc \u0641\u06cc\u0635\u0644\u0648\u06ba \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u06a9\u0627\u0641\u06cc \u06c1\u06d2\u06d4 \u0631\u06cc\u06af\u0648\u0644\u06cc\u0679\u0631\u06cc \u06cc\u0627 \u0637\u0628\u06cc \u0633\u06cc\u0627\u0642 \u0648 \u0633\u0628\u0627\u0642 \u0645\u06cc\u06ba \u0628\u0691\u06d2 \u0646\u0645\u0648\u0646\u0648\u06ba \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u0648\u06af\u06cc\u06d4<\/p>\n
      \u062c\u0628 \u0628\u06c1\u062a\u0631 \u0645\u0627\u0688\u0644\u0646\u06af \u0646\u0627\u06a9\u0627\u0645 \u06c1\u0648\u062c\u0627\u062a\u06cc \u06c1\u06d2\u06d4<\/h2>\n
      CATE \u0645\u0627\u0688\u0644 \u067e\u0631\u06a9\u0634\u0634 \u062f\u06a9\u06be\u0627\u0626\u06cc \u062f\u06cc\u062a\u0627 \u06c1\u06d2 \u06a9\u06cc\u0648\u0646\u06a9\u06c1 \u06cc\u06c1 \u0645\u0633\u0644\u0633\u0644\u060c \u0627\u0646\u0641\u0631\u0627\u062f\u06cc \u0633\u06a9\u0648\u0631 \u062a\u06cc\u0627\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 CATE \u067e\u0631 \u0645\u0628\u0646\u06cc \u067e\u0627\u0644\u06cc\u0633\u06cc\u0648\u06ba \u06a9\u0648 \u0645\u062a\u0639\u06cc\u0646 \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2\u060c \u0622\u067e \u06a9\u0648 \u0686\u0627\u0631 \u0646\u0627\u06a9\u0627\u0645\u06cc \u06a9\u06d2 \u0637\u0631\u06cc\u0642\u0648\u06ba \u067e\u0631 \u0648\u0627\u0636\u062d \u0637\u0648\u0631 \u067e\u0631 \u062a\u0648\u062c\u06c1 \u062f\u06cc\u0646\u06cc \u0686\u0627\u06c1\u06cc\u06d2\u06d4<\/p>\n
      1. \u067e\u062a\u0644\u06d2 \u062d\u0635\u06d2 (\u0627\u0648\u0648\u0631\u0644\u06cc\u067e \u06a9\u06cc \u062e\u0644\u0627\u0641 \u0648\u0631\u0632\u06cc)<\/h3>\n
      \u06c1\u0644\u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 CATE \u06a9\u0627 \u062a\u062e\u0645\u06cc\u0646\u06c1 13,451 \u0639\u0644\u0627\u062c \u0634\u062f\u06c1 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0645\u06cc\u06ba \u0633\u06d2 12%\u060c \u06cc\u0627 \u062a\u0642\u0631\u06cc\u0628\u0627\u064b 1,614 \u06c1\u06d2\u06d4 \u06cc\u06c1 \u06a9\u0644\u0627\u0633 \u0644\u06cc\u0648\u0644 \u06a9\u06cc \u0627\u0648\u0633\u0637 \u06a9\u0627 \u067e\u062a\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0627\u0641\u06cc \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u0627\u0686\u06be\u06cc \u062e\u0627\u0635\u06cc \u0642\u062f\u0631\u0648\u06ba \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u0644\u0627\u0633 \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u0627\u0646\u0641\u0631\u0627\u062f\u06cc \u0633\u0637\u062d \u06a9\u06d2 \u0627\u062b\u0631\u0627\u062a \u06a9\u0627 \u0645\u0639\u062a\u0628\u0631 \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0627\u0641\u06cc \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u06d4<\/p>\n
      \u0627\u06af\u0631 \u0639\u0644\u0627\u062c \u06a9\u06d2 \u0628\u0627\u0632\u0648 \u06a9\u06d2 \u0641\u06cc\u0686\u0631 \u0627\u0633\u067e\u06cc\u0633 \u0648\u0627\u0644\u06d2 \u0639\u0644\u0627\u0642\u06d2 \u0645\u06cc\u06ba \u06a9\u0648\u0631\u06cc\u062c \u06a9\u0645 \u06c1\u06d2\u060c \u062a\u0648 CATE \u0648\u06c1\u0627\u06ba \u0632\u06cc\u0627\u062f\u06c1 \u062a\u063a\u06cc\u0631 \u06a9\u0627 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u0644\u06af\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06cc\u06a9 \u0645\u0627\u0688\u0644 \u06c1\u0645\u0648\u0627\u0631 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc\u0627\u06ba \u0648\u0627\u067e\u0633 \u06a9\u0631 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u0627\u0646 \u06a9\u06d2 \u067e\u06cc\u0686\u06be\u06d2 \u062a\u062c\u0631\u0628\u0627\u062a\u06cc \u062a\u0639\u0627\u0648\u0646 \u06a9\u0645\u0632\u0648\u0631 \u06c1\u0648 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
      \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u06a9\u0627 \u062a\u0639\u06cc\u0646 \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2\u060c \u0633\u0628 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 CATE \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06cc \u0641\u0639\u0627\u0644 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0648 \u0686\u06cc\u06a9 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u0628\u0646\u0627\u0626\u06cc\u06ba \u06a9\u06c1 \u06c1\u0631 \u0639\u0644\u0627\u0642\u06d2 \u0645\u06cc\u06ba \u067e\u0631\u0648\u0633\u06cc\u0633\u0646\u06af \u0627\u0648\u0631 \u06a9\u0646\u0679\u0631\u0648\u0644 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a \u0645\u0648\u062c\u0648\u062f \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u0644\u06a9\u06cc\u0631\u06cc \u0631\u062c\u0639\u062a \u06a9\u0627 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u062a\u0631\u0628\u06cc\u062a \u06a9\u06cc \u062d\u062f \u0633\u06d2 \u0628\u0627\u06c1\u0631 \u0622\u0633\u0627\u0646\u06cc \u0633\u06d2 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06af\u0631 \u0645\u0627\u0688\u0644 \u06a9\u0633\u06cc \u0627\u06cc\u0633\u06d2 \u0635\u0627\u0631\u0641 \u06a9\u0648 \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc \u06a9\u06cc \u06af\u0626\u06cc CATE \u062a\u0641\u0648\u06cc\u0636 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0633 \u06a9\u06cc \u062e\u0635\u0648\u0635\u06cc\u062a \u06a9\u06cc \u0642\u062f\u0631\u06cc\u06ba \u0627\u06cc\u0633\u06d2 \u062e\u0637\u06d2 \u0645\u06cc\u06ba \u06c1\u06cc\u06ba \u062c\u0633 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 \u0628\u0627\u0632\u0648 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u0648\u0626\u06cc \u062a\u0631\u0628\u06cc\u062a\u06cc \u0688\u06cc\u0679\u0627 \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u060c \u062a\u0648 \u0627\u0633 \u062a\u062e\u0645\u06cc\u0646\u06d2 \u0645\u06cc\u06ba \u062a\u062c\u0631\u0628\u0627\u062a\u06cc \u062a\u0639\u0627\u0648\u0646 \u06a9\u0627 \u0641\u0642\u062f\u0627\u0646 \u06c1\u06d2\u06d4<\/p>\n
      \u0646\u06cc\u0633\u0679\u0688 \u0645\u0641\u0631\u0648\u0636\u06d2 \u062e\u0648\u062f \u0628\u062e\u0648\u062f \u0646\u0627\u06a9\u0627\u0645 \u06c1\u0648\u062c\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0645\u0627\u0688\u0644 \u0627\u06cc\u06a9 \u0646\u0645\u0628\u0631 \u0644\u0648\u0679\u0627\u062a\u0627 \u06c1\u06d2\u060c \u0644\u06cc\u06a9\u0646 P(T=1|X=x) \u0627\u0633 \u0639\u0644\u0627\u0642\u06d2 \u0645\u06cc\u06ba \u062a\u0642\u0631\u06cc\u0628\u0627\u064b 0 \u06cc\u0627 1 \u06c1\u06d2\u060c \u0627\u0633 \u0644\u06cc\u06d2 CATE \u06a9\u06cc \u0634\u0646\u0627\u062e\u062a \u0646\u06c1\u06cc\u06ba \u06a9\u06cc \u06af\u0626\u06cc \u06c1\u06d2\u06d4<\/p>\n
      \u0627\u067e\u0646\u06cc CATE \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc\u0648\u06ba \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u067e\u0646\u06d2 \u067e\u0631\u0648\u067e\u06cc\u0646\u0633\u06cc\u0679\u06cc \u0633\u06a9\u0648\u0631 \u06a9\u0648 \u0686\u06cc\u06a9 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0622\u0641 \u067e\u0631\u067e\u06cc\u0646\u0633\u0679\u06cc \u06a9\u0644\u067e\u0633 \u06cc\u0627 \u0641\u0644\u06cc\u06af \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u06a9\u06cc \u062c\u0627\u0646\u0686 \u06a9\u0631\u06cc\u06ba\u06d4 [0.05, 0.95].<\/p>\n
      3. \u0686\u06be\u0648\u0679\u06d2 k \u067e\u0631 \u06a9\u0646\u06cc \u0634\u0648\u0631<\/h3>\n
      Qini \u0645\u0646\u062d\u0646\u06cc \u062e\u0637\u0648\u0637 \u0628\u06c1\u062a \u0686\u06be\u0648\u0679\u06d2 k (\u0627\u0648\u067e\u0631 5% \u06cc\u0627 \u06a9\u0645) \u067e\u0631 \u0634\u0648\u0631 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0627\u06af\u0631 \u0627\u06cc\u06a9 \u062a\u0634\u062e\u06cc\u0635\u06cc \u06af\u0631\u0648\u067e \u0645\u06cc\u06ba \u0635\u0631\u0641 \u0686\u0646\u062f \u0633\u0648 \u0635\u0627\u0631\u0641\u06cc\u0646 \u06c1\u06cc\u06ba\u060c \u062a\u0648 \u0627\u0633 \u06af\u0631\u0648\u067e \u0645\u06cc\u06ba \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u06cc \u062c\u0627\u0646\u06d2 \u0648\u0627\u0644\u06cc \u062a\u0639\u062f\u0627\u062f \u0627\u062a\u0646\u06cc \u06a9\u0645 \u06c1\u0648 \u0633\u06a9\u062a\u06cc \u06c1\u06d2 \u06a9\u06c1 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u0634\u062f\u06c1 \u0627\u0636\u0627\u0641\u06c1 \u0646\u0645\u0648\u0646\u06d2 \u0644\u06cc\u0646\u06d2 \u06a9\u06d2 \u0634\u0648\u0631 \u0633\u06d2 \u063a\u0644\u0628\u06c1 \u0631\u06a9\u06be\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
      \u067e\u06c1\u0644\u06d2 \u0633\u06d2 \u0637\u06d2 \u0634\u062f\u06c1 \u0631\u0648\u0644 \u0622\u0624\u0679 \u06a9\u0627 \u0641\u06cc\u0635\u0644\u06c1 20% \u0633\u06d2 50% Qini \u0631\u06cc\u0646\u062c \u06a9\u06d2 \u0644\u06cc\u06d2 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u060c \u062c\u06c1\u0627\u06ba \u0633\u06af\u0646\u0644\u0632 \u0632\u06cc\u0627\u062f\u06c1 \u0645\u0633\u062a\u062d\u06a9\u0645 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a\u06cc \u062a\u0631\u062a\u06cc\u0628\u0627\u062a \u0645\u06cc\u06ba\u060c \u0628\u0691\u06d2 k \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0639\u0644\u06cc Qini \u0642\u062f\u0631\u06cc\u06ba (\u062c\u06cc\u0633\u06d2 \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u0645\u06cc\u06ba \u0633\u0628 \u0633\u06d2 \u0627\u0648\u067e\u0631 70% \u0645\u06cc\u06ba +0.1454) \u062d\u0642\u06cc\u0642\u06cc CATE \u0633\u06af\u0646\u0644 \u06a9\u0648 \u0686\u06be\u067e\u0627\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0627\u0646\u062a\u062e\u0627\u0628\u06cc \u062a\u0639\u0635\u0628 \u06a9\u06cc \u0639\u06a9\u0627\u0633\u06cc \u06a9\u0631 \u0633\u06a9\u062a\u06cc \u06c1\u06cc\u06ba\u06d4 \u0644\u0641\u0679 \u06a9\u06cc \u0642\u062f\u0631\u0648\u06ba \u06a9\u06cc \u062a\u0634\u0631\u06cc\u062d \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u06c1\u0631 \u0679\u0627\u067e \u06a9\u06d2 \u06af\u0631\u0648\u067e \u06a9\u06d2 \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc \u06a9\u0627 \u062c\u0627\u0626\u0632\u06c1 \u0644\u06cc\u06ba\u06d4<\/p>\n
      4. CATE \u0645\u0627\u0688\u0644 \u06a9\u06cc \u0627\u0648\u0648\u0631 \u0641\u0679\u0646\u06af<\/h3>\n
      \u06a9\u0648\u0626\u06cc \u0631\u0627\u0633\u062a\u06c1 \u0646\u06c1\u06cc\u06ba LinearRegression<\/code> \u06cc\u06c1\u0627\u06ba \u0639\u0644\u0627\u062c \u0634\u062f\u06c1 \u0628\u0627\u0632\u0648 \u067e\u0631 \u062a\u0631\u0628\u06cc\u062a \u0633\u06d2 13,451 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a \u0627\u0648\u0631 \u0686\u0627\u0631 \u062e\u0635\u0648\u0635\u06cc\u0627\u062a \u062d\u0627\u0635\u0644 \u06c1\u0648\u0626\u06cc\u06ba: \u06a9\u0645\u0641\u0631\u0679 \u0645\u0627\u0631\u062c\u0646\u061b \u0644\u06a9\u06cc\u0631\u06cc \u0631\u06cc\u06af\u0631\u06cc\u0634\u0646 \u06a9\u0648 \u06af\u0631\u06cc\u0688\u06cc\u0646\u0679 \u0628\u0648\u0633\u0679\u0646\u06af \u06a9\u06d2 \u0633\u0627\u062a\u06be \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0631\u0646\u0627 \u0627\u0648\u0631 30 \u200b\u200b\u0641\u06cc\u0686\u0631\u0632 \u0634\u0627\u0645\u0644 \u06a9\u0631\u0646\u0627 \u0679\u0631\u06cc\u0646\u0646\u06af \u0634\u0648\u0631 \u0633\u06d2 \u0645\u0646\u0633\u0648\u0628 \u067e\u0631\u0648\u0633\u06cc\u0633\u0646\u06af \u0627\u062b\u0631\u0627\u062a \u06a9\u06cc \u0627\u0648\u0648\u0631 \u0641\u0679\u0646\u06af \u06a9\u0627 \u0628\u0627\u0639\u062b \u0628\u0646 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4 \u062a\u0631\u0628\u06cc\u062a\u06cc \u0633\u06cc\u0679 \u0645\u06cc\u06ba CATE \u06a9\u06cc \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc\u0627\u06ba \u0628\u06c1\u062a \u06c1\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u062f\u06a9\u06be\u0627\u0626\u06cc \u062f\u06cc\u062a\u06cc \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u06c1\u0648\u0644\u0688 \u0622\u0624\u0679 \u0633\u06cc\u0679 \u0645\u06cc\u06ba \u0639\u0627\u0644\u0645\u06cc \u0627\u0648\u0633\u0637 \u06a9\u06cc \u0637\u0631\u0641 \u0631\u062c\u0648\u0639 \u06a9\u0631\u062a\u06cc \u06c1\u06cc\u06ba\u06d4 CATE \u0645\u0627\u0688\u0644 \u0627\u0633 \u0648\u0642\u062a \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0628 \u06cc\u06c1 \u0645\u062e\u0635\u0648\u0635 \u0627\u0635\u0644\u0627\u062d\u0627\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u062f\u0631\u062c\u06d2 \u06a9\u06cc \u0627\u0648\u0633\u0637 \u0633\u06d2 \u0622\u06af\u06d2 \u0646\u06a9\u0644 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0631\u06cc\u0644\u06cc\u0632 \u06a9\u06d2 \u0642\u0648\u0627\u0639\u062f \u0628\u0646\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u0631\u06a9\u06be\u06d2 \u06af\u0626\u06d2 \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679\u0633 \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n
      \u0622\u06af\u06d2 \u06a9\u06cc\u0627 \u06a9\u0631\u0646\u0627 \u06c1\u06d2\u06d4<\/h2>\n
      \u0645\u0646\u062f\u0631\u062c\u06c1 \u0628\u0627\u0644\u0627 \u0639\u0645\u0644 \u062f\u0631\u0622\u0645\u062f \u0628\u06cc\u0631\u0648\u0646\u06cc \u0627\u0636\u0627\u0641\u06c1 \u0644\u0627\u0626\u0628\u0631\u06cc\u0631\u06cc\u0648\u06ba \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u0628\u0646\u0627\u06cc\u0627 \u06af\u06cc\u0627 \u062a\u06be\u0627\u060c \u0644\u06c1\u0630\u0627 \u0622\u067e \u0628\u0627\u0644\u06a9\u0644 \u062f\u06cc\u06a9\u06be \u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06c1 \u06c1\u0631 \u0642\u062f\u0645 \u067e\u0631 \u06a9\u06cc\u0627 \u062d\u0633\u0627\u0628 \u06a9\u06cc\u0627 \u062c\u0627 \u0631\u06c1\u0627 \u06c1\u06d2\u06d4 \u067e\u06cc\u062f\u0627\u0648\u0627\u0631\u06cc \u0645\u0642\u0627\u0635\u062f \u06a9\u06d2 \u0644\u06cc\u06d2 causalml<\/code> \u0627\u0648\u0631 econml<\/code> \u06c1\u0645 \u062f\u0648\u0646\u0648\u06ba \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u0648\u06ba \u06a9\u06d2 \u0628\u06c1\u062a\u0631 \u0648\u0631\u0698\u0646 \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0628\u0634\u0645\u0648\u0644 \u0627\u06cc\u06a9 \u062f\u0631\u062e\u062a \u067e\u0631 \u0645\u0628\u0646\u06cc \u0679\u06cc-\u0644\u0631\u0646\u0631\u060c \u0627\u06cc\u06a9 \u062f\u0648\u06af\u0646\u0627 \u0637\u0627\u0642\u062a\u0648\u0631 X-\u0644\u0627\u0631\u0646\u0631\u060c \u0627\u0648\u0631 \u0627\u06cc\u06a9 \u0627\u06cc\u0645\u0627\u0646\u062f\u0627\u0631 \u06a9\u0627\u0632\u0644 \u0641\u0627\u0631\u0633\u0679 \u062c\u0648 \u0632\u06cc\u0627\u062f\u06c1 \u0641\u0679\u0646\u06af \u06a9\u0648 \u06a9\u0645 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062a\u0631\u0628\u06cc\u062a \u0627\u0648\u0631 \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06a9\u06d2 \u0646\u0645\u0648\u0646\u0648\u06ba \u06a9\u0648 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u062f\u0648\u0646\u0648\u06ba \u0644\u0627\u0626\u0628\u0631\u06cc\u0631\u06cc\u0627\u06ba \u06cc\u06c1\u0627\u06ba \u0628\u0646\u0627\u0626\u06d2 \u06af\u0626\u06d2 \u0627\u06cc\u06a9 \u06c1\u06cc \u062a\u0635\u0648\u0631\u0627\u062a\u06cc \u0688\u06be\u0627\u0646\u0686\u06d2 \u06a9\u06cc \u067e\u06cc\u0631\u0648\u06cc \u06a9\u0631\u062a\u06cc \u06c1\u06cc\u06ba\u06d4<\/p>\n
      causalml<\/code>    \u0627\u0633 \u0645\u06cc\u06ba \u067e\u0631\u0648\u0688\u06a9\u0634\u0646 \u06af\u0631\u06cc\u0688 \u06a9\u0646\u06cc \u0648\u06a9\u0631 \u0627\u0648\u0631 \u0627\u06cc\u0631\u06cc\u0627 \u0627\u0646\u0688\u0631 \u062f\u06cc \u0631\u0627\u0626\u0632 \u06a9\u0631\u06cc\u0648 (AUUC) \u0645\u06cc\u0679\u0631\u06a9 \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0634\u0627\u0645\u0644 \u06c1\u06d2\u060c \u062c\u0648 \u06a9\u0646\u06cc \u0648\u06a9\u0631 \u06a9\u0648 \u0627\u06cc\u06a9 \u0648\u0627\u062d\u062f \u062a\u0642\u0627\u0628\u0644\u06cc \u0627\u0639\u062f\u0627\u062f \u0648 \u0634\u0645\u0627\u0631 \u062a\u06a9 \u06af\u06be\u0679\u0627 \u062f\u06cc\u062a\u0627 \u06c1\u06d2\u06d4 A\/B \u0641\u0631\u06cc\u0645 \u0648\u0631\u06a9 \u0645\u06cc\u06ba \u0628\u06c1\u062a\u0631\u06cc \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u0627 \u0645\u0648\u0627\u0632\u0646\u06c1 \u0686\u0644\u0627\u062a\u06d2 \u0648\u0642\u062a\u060c AUUC \u0627\u06cc\u06a9 \u0645\u0639\u06cc\u0627\u0631\u06cc \u0644\u06cc\u0688\u0631 \u0628\u0648\u0631\u0688 \u0645\u06cc\u0679\u0631\u06a9 \u06c1\u06d2\u06d4<\/p>\n
      \u0646\u0627\u0645 \u062f\u06cc\u0646\u06d2 \u06a9\u06d2 \u0642\u0627\u0628\u0644 \u0627\u06cc\u06a9 \u0633\u0627\u062e\u062a\u06cc \u062d\u062f: \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u0646\u06d2 SUTVA \u0641\u0631\u0636 \u06a9\u06cc\u0627 \u06c1\u06d2\u06d4 \u0627\u0633 \u06a9\u0627 \u0645\u0637\u0644\u0628 \u06c1\u06d2 \u06a9\u06c1 \u06c1\u0631 \u0635\u0627\u0631\u0641 \u06a9\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u0627 \u0627\u0646\u062d\u0635\u0627\u0631 \u0635\u0631\u0641 \u0627\u0646 \u06a9\u06d2 \u0627\u067e\u0646\u06d2 \u0639\u0644\u0627\u062c \u06a9\u06cc \u062d\u06cc\u062b\u06cc\u062a \u067e\u0631 \u06c1\u06d2\u06d4 \u06a9\u0627\u0645 \u06a9\u06cc \u062c\u06af\u06c1 \u067e\u0631 \u0645\u0628\u0646\u06cc AI \u0645\u0635\u0646\u0648\u0639\u0627\u062a \u0645\u06cc\u06ba\u060c \u06cc\u06c1 \u0645\u0641\u0631\u0648\u0636\u06d2 \u0627\u06a9\u062b\u0631 \u063a\u0644\u0637 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0627\u06cc\u06a9 \u06c1\u06cc \u0648\u0631\u06a9 \u0627\u0633\u067e\u06cc\u0633 \u06a9\u06d2 \u0635\u0627\u0631\u0641\u06cc\u0646 \u0627\u06cc\u06a9 \u0645\u0634\u062a\u0631\u06a9\u06c1 \u0645\u0627\u062d\u0648\u0644 \u06a9\u0627 \u0627\u0634\u062a\u0631\u0627\u06a9 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0648\u0631 \u0627\u06cc\u06a9 \u0635\u0627\u0631\u0641 \u06a9\u0627 \u0633\u0644\u0648\u06a9 \u0645\u0634\u062a\u0631\u06a9\u06c1 \u0622\u0624\u0679 \u067e\u0679\u060c \u0631\u062f\u0651\u0639\u0645\u0644 \u06a9\u06d2 \u0628\u062f\u0644\u06d2 \u06c1\u0648\u0626\u06d2 \u0646\u0645\u0648\u0646\u0648\u06ba\u060c \u06cc\u0627 \u06a9\u0627\u0645 \u06a9\u06cc \u062c\u06af\u06c1 \u06a9\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u06c1 \u062d\u0631\u06a9\u06cc\u0627\u062a \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u0679\u06cc\u0645 \u06a9\u06d2 \u0627\u0631\u0627\u06a9\u06cc\u0646 \u06a9\u0648 \u0645\u062a\u0627\u062b\u0631 \u06a9\u0631 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n
      \u0627\u06af\u0631 \u0627\u0633 \u0642\u0633\u0645 \u06a9\u06cc \u0645\u062f\u0627\u062e\u0644\u062a \u06a9\u0627 \u0634\u0628\u06c1 \u06c1\u06d2 \u062a\u0648\u060c \u0627\u06cc\u06a9 DR-Learner \u0645\u062e\u062a\u0644\u0641 \u0642\u0633\u0645 \u062c\u0648 \u06af\u0631\u0648\u067e \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u062a\u0639\u0644\u0642 \u06a9\u0648 CATE \u062a\u062e\u0645\u06cc\u0646\u0648\u06ba \u0645\u06cc\u06ba \u067e\u06be\u06cc\u0644\u0627\u062a\u0627 \u06c1\u06d2\u060c \u0632\u06cc\u0627\u062f\u06c1 \u062d\u0642\u06cc\u0642\u062a \u067e\u0633\u0646\u062f\u0627\u0646\u06c1 \u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u06a9\u06cc \u062d\u062f \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u0645\u0639\u06cc\u0627\u0631\u06cc \u0679\u06cc \u0644\u0631\u0646\u0631\u0632 \u0627\u0648\u0631 \u0627\u06cc\u06a9\u0633 \u0644\u0631\u0646\u0631\u0632 \u062a\u0645\u0627\u0645 \u0645\u0634\u0627\u06c1\u062f\u0627\u062a \u06a9\u0648 \u0622\u0632\u0627\u062f \u062e\u06cc\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633 \u0637\u0631\u062d \u062c\u0628 \u0648\u0631\u06a9 \u0627\u0633\u067e\u06cc\u0633 \u0644\u06cc\u0648\u0644 \u06a9\u06d2 \u0639\u0648\u0627\u0645\u0644 \u06a9\u0627\u0645 \u0645\u06cc\u06ba \u0622\u062a\u06d2 \u06c1\u06cc\u06ba \u062a\u0648 \u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u06a9\u0648 \u06a9\u0645 \u0633\u0645\u062c\u06be\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n
      \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u06a9\u0627 \u0633\u0627\u062a\u06be\u06cc \u0630\u062e\u06cc\u0631\u06c1 github.com\/RudrenduPaul\/product-experimentation-causal-inference-genai-llm\/tree\/main\/08_uplift_modeling \u067e\u0631 \u06c1\u06d2\u06d4 \u0631\u06cc\u067e\u0648\u0632\u0679\u0631\u06cc \u06a9\u0648 \u06a9\u0644\u0648\u0646 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0628\u0646\u0627\u0626\u06cc\u06ba: --n-users 50000 --seed 42<\/code>\u0627\u0648\u0631 \u0686\u0644\u0627\u0626\u06cc\u06ba uplift_demo.py<\/code> \u0627\u0633 \u0679\u06cc\u0648\u0679\u0648\u0631\u06cc\u0644 \u0633\u06d2 \u062a\u0645\u0627\u0645 \u0646\u062a\u0627\u0626\u062c \u062f\u0648\u0628\u0627\u0631\u06c1 \u067e\u06cc\u0634 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n
      ATE \u0648\u06c1 \u0646\u0645\u0628\u0631 \u06c1\u06d2 \u062c\u0633 \u06a9\u06cc \u0622\u067e \u06a9\u0648 \u06cc\u06c1 \u0641\u06cc\u0635\u0644\u06c1 \u06a9\u0631\u0646\u06d2 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2 \u06a9\u06c1 \u0641\u06cc\u0686\u0631 \u0628\u0646\u0627\u0646\u0627 \u06c1\u06d2 \u06cc\u0627 \u0646\u06c1\u06cc\u06ba\u06d4 CATE \u0648\u06c1 \u0646\u0645\u0628\u0631 \u06c1\u06d2 \u062c\u0648 \u06cc\u06c1 \u0641\u06cc\u0635\u0644\u06c1 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0636\u0631\u0648\u0631\u06cc \u06c1\u06d2 \u06a9\u06c1 \u0627\u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u06a9\u0633 \u06a9\u0648 \u0645\u0644\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06cc\u06a9 \u062f\u0627\u0646\u06d2 \u062f\u0627\u0631 \u0631\u0648\u0644 \u0622\u0624\u0679 \u062c\u0648 \u06a9\u06c1 54% \u0635\u0627\u0631\u0641\u06cc\u0646 \u06a9\u06d2 \u0639\u0644\u0627\u062c \u067e\u0631 \u062a\u0648\u062c\u06c1 \u0645\u0631\u06a9\u0648\u0632 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0648 \u06a9\u06c1 \u0633\u0628 \u0633\u06d2 \u0645\u0636\u0628\u0648\u0637 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u0648\u0627\u0644\u06d2 \u0631\u062f\u0639\u0645\u0644 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u06cc\u06a9 \u06c1\u06cc \u062e\u0635\u0648\u0635\u06cc\u062a \u06a9\u0648 \u06c1\u0631 \u06a9\u0633\u06cc \u06a9\u06d2 \u0633\u0627\u0645\u0646\u06d2 \u0644\u0627\u0646\u06d2 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0646\u062a\u0627\u0626\u062c \u062f\u06d2 \u06af\u0627\u06d4 \u06cc\u0648\u0646\u06cc\u0641\u0627\u0631\u0645 \u0631\u0648\u0644 \u0622\u0624\u0679 \u067e\u0627\u0644\u06cc\u0633\u06cc \u06a9\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06c1\u06d2\u06d4 \u0627\u0637\u0644\u0627\u0639 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06cc\u06ba\u06d4<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"
      \u0622\u067e \u06a9\u06d2 LLM \u067e\u0631\u0648\u0688\u06a9\u0679 \u06a9\u06d2 \u062a\u062c\u0631\u0628\u06d2 \u06a9\u06d2 \u0645\u062b\u0628\u062a \u0646\u062a\u0627\u0626\u062c \u0628\u0631\u0622\u0645\u062f \u06c1\u0648\u0626\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u062a\u0648\u0642\u0639 \u06c1\u06d2 \u06a9\u06c1 \u06a9\u0627\u0645 \u06a9\u06cc \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u06cc \u0634\u0631\u062d \u0645\u06cc\u06ba 8% \u0627\u0636\u0627\u0641\u06c1 \u06c1\u0648\u06af\u0627\u06d4 \u062c\u0628 \u0622\u067e \u06a9\u0648\u0626\u06cc \u062e\u0635\u0648\u0635\u06cc\u062a \u062c\u0627\u0631\u06cc \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u062a\u0648 \u0642\u06cc\u0627\u062f\u062a \u062c\u0634\u0646 \u0645\u0646\u0627\u062a\u06cc \u06c1\u06d2\u06d4 \u062a\u06cc\u0646 \u0645\u0627\u06c1 \u0628\u0639\u062f\u060c \u0627\u06c1\u0645 \u0627\u0634\u0627\u0631\u06d2 \u0628\u0645\u0634\u06a9\u0644 \u0645\u0646\u062a\u0642\u0644 \u06c1\u0648\u0626\u06d2 \u06c1\u06cc\u06ba\u06d4 \u062a\u062c\u0631\u0628\u06c1 \u0634\u0645\u0627\u0631\u06cc\u0627\u062a\u06cc \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u06d2 \u062f\u0631\u0633\u062a \u062a\u06be\u0627\u06d4 \u0627\u0633 \u0646\u06d2 \u0635\u0631\u0641 […]<\/p>\n","protected":false},"author":7,"featured_media":26499,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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