{"id":68748,"date":"2026-01-30T17:05:17","date_gmt":"2026-01-30T09:05:17","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68748.html"},"modified":"2026-01-30T17:05:17","modified_gmt":"2026-01-30T09:05:17","slug":"ab%e5%ae%9e%e9%aa%8c%e7%9a%84%e9%ab%98%e9%98%b6%e6%8a%80%e6%b3%95%ef%bc%88%e4%ba%8c%ef%bc%89%ef%bc%9a%e5%bc%82%e8%b4%a8%e6%80%a7%e5%88%86%e6%9e%90%e5%88%a9%e5%99%a8-%e7%ba%bf%e6%80%a7","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68748.html","title":{"rendered":"AB\u5b9e\u9a8c\u7684\u9ad8\u9636\u6280\u6cd5\uff08\u4e8c\uff09\uff1a\u5f02\u8d28\u6027\u5206\u6790\u5229\u5668\u2014\u2014\u7ebf\u6027\u56de\u5f52"},"content":{"rendered":"

\u2014\u5173\u6ce8\u4f5c\u8005，\u9001A\/B\u5b9e\u9a8c\u5b9e\u6218\u5de5\u5177\u5305<\/p>\n

\n

\u5f88\u591a\u521a\u5165\u884c\u7684\u6570\u636e\u5206\u6790\u5e08\u6216\u5de5\u7a0b\u5e08，\u5bf9 A\/B \u5b9e\u9a8c\u7684\u7406\u89e3\u5f80\u5f80\u505c\u7559\u5728 T \u68c0\u9a8c（T-test）\u7684\u5c42\u9762：\u628a\u6d41\u91cf\u5206\u4e24\u534a，\u7b97\u7b97\u5747\u503c，\u770b\u770b P \u503c，\u7ed3\u675f\u3002<\/p>\n

\u4f46\u5728\u590d\u6742\u7684\u4e1a\u52a1\u573a\u666f\u4e0b，T \u68c0\u9a8c\u5f80\u5f80\u663e\u5f97\u201c\u706b\u529b\u4e0d\u8db3\u201d\u3002\u6bd4\u5982，\u5f53\u4f60\u60f3\u77e5\u9053\u201c\u8fd9\u4e2a\u7b56\u7565\u662f\u4e0d\u662f\u53ea\u5bf9\u8001\u7528\u6237\u6709\u6548？\u201d\u6216\u8005\u201c\u4e3a\u4ec0\u4e48\u6574\u4f53\u4e0d\u663e\u8457，\u4f46\u67d0\u4e9b\u57ce\u5e02\u7684\u6570\u636e\u5374\u5728\u66b4\u6da8？\u201d\u65f6，T \u68c0\u9a8c\u5c31\u6349\u895f\u89c1\u8098\u4e86\u3002<\/p>\n

\u8fd9\u65f6\u5019，\u4f60\u9700\u8981\u796d\u51fa\u7edf\u8ba1\u63a8\u65ad\u7684\u91cd\u578b\u6b66\u5668：\u7ebf\u6027\u56de\u5f52 (Linear Regression)\u3002<\/p>\n

\u522b\u8bef\u4f1a，\u6211\u4e0d\u662f\u8ba9\u4f60\u7528\u5b83\u505a\u9884\u6d4b，\u800c\u662f\u7528\u5b83\u505a\u56e0\u679c\u63a8\u65ad\u3002\u5728 A\/B \u5b9e\u9a8c\u7684\u9ad8\u9636\u5206\u6790\u4e2d，\u7ebf\u6027\u56de\u5f52\u4e0d\u4ec5\u80fd\u66ff\u4ee3 T \u68c0\u9a8c，\u66f4\u662f\u5f02\u8d28\u6027\u5206\u6790 (Heterogeneity Analysis) \u7684\u6838\u5fc3\u5de5\u5177\u3002<\/p>\n

\n

\u4e00\u3001\u6253\u7834\u8ba4\u77e5：\u56de\u5f52\u4e0d\u53ea\u662f\u9884\u6d4b<\/h4>\n

\u5728\u673a\u5668\u5b66\u4e60\u8bed\u5883\u4e0b，\u6211\u4eec\u4e60\u60ef\u7528\u56de\u5f52\u53bb\u62df\u5408\u4e00\u6761\u7ebf\u6765\u9884\u6d4b\u672a\u6765\u3002\u4f46\u5728 A\/B \u5b9e\u9a8c\u8bed\u5883\u4e0b，\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570（Coefficient）\u624d\u662f\u4e3b\u89d2\u3002<\/p>\n

\u5f53\u6211\u4eec\u6784\u5efa\u4e00\u4e2a\u5305\u542b\u5e72\u9884\u53d8\u91cf <\/p>\n

T<\/p>\n

T<\/p>\n

<\/span><\/span>T<\/span><\/span><\/span><\/span><\/span>（Treatment，0\u62161）\u7684\u56de\u5f52\u6a21\u578b\u65f6，<\/p>\n

T<\/p>\n

T<\/p>\n

<\/span><\/span>T<\/span><\/span><\/span><\/span><\/span> \u7684\u7cfb\u6570 <\/p>\n

\u03b2<\/p>\n

\\\\beta<\/p>\n

<\/span><\/span>\u03b2<\/span><\/span><\/span><\/span><\/span> \u5176\u5b9e\u5c31\u662f\u5b9e\u9a8c\u7ec4\u76f8\u5bf9\u4e8e\u5bf9\u7167\u7ec4\u7684\u5e73\u5747\u5904\u7406\u6548\u5e94 (ATE, Average Treatment Effect)\u3002<\/p>\n

\u7b80\u5355\u6765\u8bf4：T \u68c0\u9a8c\u80fd\u505a\u7684\u4e8b，\u56de\u5f52\u90fd\u80fd\u505a；T \u68c0\u9a8c\u505a\u4e0d\u4e86\u7684（\u5982\u5f15\u5165\u534f\u53d8\u91cf\u3001\u5206\u6790\u4ea4\u4e92\u6548\u5e94），\u56de\u5f52\u4e5f\u80fd\u505a\u3002<\/p>\n

\u4e8c\u3001\u7ebf\u6027\u56de\u5f52\u7684\u56db\u5927\u5047\u8bbe<\/h4>\n

\u5728\u4f7f\u7528\u8fd9\u628a\u5229\u5668\u4e4b\u524d，\u5fc5\u987b\u5148\u4e86\u89e3\u5b83\u7684\u5e95\u7ebf\u3002\u7ebf\u6027\u56de\u5f52\u6a21\u578b（OLS）\u7684\u6709\u6548\u6027\u5efa\u7acb\u5728\u56db\u4e2a\u6838\u5fc3\u5047\u8bbe\u4e4b\u4e0a，\u8fdd\u80cc\u8fd9\u4e9b\u5047\u8bbe\u53ef\u80fd\u5bfc\u81f4\u63a8\u65ad\u5931\u6548\u3002<\/p>\n

  • \u7ebf\u6027\u6027 (Linearity) \u56e0\u53d8\u91cf \n

    Y<\/p>\n

    Y<\/p>\n

    <\/span><\/span>Y<\/span><\/span><\/span><\/span><\/span> \u4e0e\u81ea\u53d8\u91cf <\/p>\n

    X<\/p>\n

    X<\/p>\n

    <\/span><\/span>X<\/span><\/span><\/span><\/span><\/span> \u4e4b\u95f4\u5b58\u5728\u7ebf\u6027\u5173\u7cfb\u3002\u8fd9\u610f\u5473\u7740\u81ea\u53d8\u91cf\u6bcf\u53d8\u5316\u4e00\u4e2a\u5355\u4f4d，\u56e0\u53d8\u91cf\u7684\u53d8\u5316\u91cf\u662f\u6052\u5b9a\u7684\u3002<\/li>\n

  • \u72ec\u7acb\u6027 (Independence) \u6837\u672c\u4e4b\u95f4\u5fc5\u987b\u662f\u76f8\u4e92\u72ec\u7acb\u7684\u3002\u5728 A\/B \u5b9e\u9a8c\u4e2d，\u8fd9\u8981\u6c42\u968f\u673a\u5206\u6d41（Randomization）\u5fc5\u987b\u4e25\u683c\u6267\u884c，\u7528\u6237\u7684\u884c\u4e3a\u4e92\u4e0d\u5e72\u6270（SUTVA \u5047\u8bbe）\u3002<\/li>\n
  • \u540c\u65b9\u5dee\u6027 (Homoscedasticity) \u8bef\u5dee\u9879\u7684\u65b9\u5dee\u5e94\u4e3a\u5e38\u6570，\u4e0d\u968f\u81ea\u53d8\u91cf \n

    X<\/p>\n

    X<\/p>\n

    <\/span><\/span>X<\/span><\/span><\/span><\/span><\/span> \u7684\u53d8\u5316\u800c\u53d8\u5316\u3002 <\/p>\n

      \n
    • \u76f4\u89c2\u7406\u89e3：\u6a21\u578b\u9884\u6d4b\u7684\u201c\u7a33\u5b9a\u6027\u201d\u5e94\u8be5\u662f\u4e00\u81f4\u7684\u3002\u4e0d\u80fd\u51fa\u73b0\u201c\u9884\u6d4b\u4f4e\u6536\u5165\u4eba\u7fa4\u5f88\u51c6，\u9884\u6d4b\u9ad8\u6536\u5165\u4eba\u7fa4\u8bef\u5dee\u5de8\u5927\u201d\u7684\u60c5\u51b5（\u5373\u201c\u5587\u53ed\u53e3\u201d\u5f62\u72b6\u7684\u6b8b\u5dee\u5206\u5e03）\u3002<\/li>\n<\/ul>\n<\/li>\n
    • \u8bef\u5dee\u6b63\u6001\u6027 (Normality of Errors) \u8bef\u5dee\u9879 \n

      \u03f5<\/p>\n

      \\\\epsilon<\/p>\n

      <\/span><\/span>\u03f5<\/span><\/span><\/span><\/span><\/span> \u5e94\u670d\u4ece\u6b63\u6001\u5206\u5e03\u3002 <\/p>\n

        \n
      • \u6ce8：\u5728\u5927\u6837\u672c\u4e0b，\u6839\u636e\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406 (CLT)，\u5373\u4f7f\u8bef\u5dee\u9879\u4e0d\u4e25\u683c\u670d\u4ece\u6b63\u6001\u5206\u5e03，\u56de\u5f52\u7cfb\u6570\u7684\u4f30\u8ba1\u901a\u5e38\u4e5f\u662f\u6e10\u8fd1\u6b63\u6001\u7684，\u4f46\u8fd9\u4f9d\u7136\u662f\u7406\u60f3\u72b6\u6001\u4e0b\u7684\u8981\u6c42\u3002<\/li>\n<\/ul>\n<\/li>\n

        \u4e09\u3001\u5f02\u8d28\u6027\u5206\u6790：\u4e3a\u4ec0\u4e48\u56de\u5f52\u6bd4\u201c\u4e0b\u94bb\u201d\u66f4\u5f3a？<\/h4>\n

        \u5f02\u8d28\u6027\u5206\u6790\u65e8\u5728\u56de\u7b54\u4e00\u4e2a\u95ee\u9898：\u5b9e\u9a8c\u6548\u679c\u5728\u4e0d\u540c\u4eba\u7fa4\u4e2d\u662f\u5426\u5b58\u5728\u663e\u8457\u5dee\u5f02？<\/p>\n

        \u6700\u76f4\u89c2\u7684\u505a\u6cd5\u662f\u4e0b\u94bb\u5206\u6790 (Drill-down)，\u4e5f\u5c31\u662f\u628a\u6570\u636e\u5207\u5f00，\u5206\u522b\u770b\u7537\u7528\u6237\u548c\u5973\u7528\u6237\u7684 Lift \u548c P \u503c\u3002\u4f46\u8fd9\u5728\u7edf\u8ba1\u4e25\u8c28\u6027\u4e0a\u5b58\u5728\u5de8\u5927\u6f0f\u6d1e\u3002<\/p>\n

        1. \u4e0b\u94bb\u5206\u6790\u7684\u9677\u9631<\/h5>\n
          \n
        • \u5047\u9634\u6027\u98ce\u9669：\u5f53\u4f60\u628a\u6570\u636e\u5207\u6210\u4e24\u534a，\u6bcf\u4e00\u7ec4\u7684\u6837\u672c\u91cf \n

          N<\/p>\n

          N<\/p>\n

          <\/span><\/span>N<\/span><\/span><\/span><\/span><\/span> \u51cf\u534a，\u65b9\u5dee\u53d8\u5927，\u7edf\u8ba1\u529f\u6548 (Power) \u66b4\u8dcc\u3002\u672c\u6765\u663e\u8457\u7684\u5dee\u5f02，\u5207\u5f00\u540e\u53ef\u80fd\u90fd\u4e0d\u663e\u8457\u4e86\u3002<\/li>\n

        • \u65e0\u6cd5\u68c0\u9a8c\u201c\u5dee\u5f02\u7684\u5dee\u5f02\u201d：\u5982\u679c\u7537\u6027\u7ec4\u63d0\u5347 5%（\u663e\u8457），\u5973\u6027\u7ec4\u63d0\u5347 2%（\u4e0d\u663e\u8457），\u4f60\u80fd\u8bf4\u201c\u7b56\u7565\u5bf9\u7537\u6027\u663e\u8457\u4f18\u4e8e\u5973\u6027\u201d\u5417？\u4e0d\u80fd\u3002\u7edf\u8ba1\u5b66\u4e0a，\u201c\u4e00\u4e2a\u663e\u8457\u4e00\u4e2a\u4e0d\u663e\u8457\u201d\u4e0d\u4ee3\u8868\u201c\u4e24\u8005\u4e4b\u95f4\u6709\u663e\u8457\u5dee\u5f02\u201d\u3002<\/li>\n<\/ul>\n
          2. \u56de\u5f52\u7684\u89e3\u6cd5：\u4ea4\u4e92\u9879 (Interaction Term)<\/h5>\n

          \u7ebf\u6027\u56de\u5f52\u901a\u8fc7\u5f15\u5165\u4ea4\u4e92\u9879，\u76f4\u63a5\u5bf9\u201c\u5dee\u5f02\u7684\u663e\u8457\u6027\u201d\u8fdb\u884c\u68c0\u9a8c\u3002<\/p>\n

          \u516c\u5f0f： <\/p>\n

          Y<\/p>\n

          =<\/p>\n

          \u03b2<\/p>\n

          0<\/p>\n

          +<\/p>\n

          \u03b2<\/p>\n

          1<\/p>\n

          T<\/p>\n

          +<\/p>\n

          \u03b2<\/p>\n

          2<\/p>\n

          X<\/p>\n

          +<\/p>\n

          \u03b2<\/p>\n

          3<\/p>\n

          (<\/p>\n

          T<\/p>\n

          \u22c5<\/p>\n

          X<\/p>\n

          )<\/p>\n

          +<\/p>\n

          \u03f5<\/p>\n

          Y = \\\\beta_0 + \\\\beta_1 T + \\\\beta_2 X + \\\\beta_3 (T \\\\cdot X) + \\\\epsilon <\/p>\n

          <\/span><\/span>Y<\/span><\/span>=<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>0<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>T<\/span><\/span>+<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>X<\/span><\/span>+<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>(<\/span>T<\/span><\/span>\u22c5<\/span><\/span><\/span><\/span>X<\/span>)<\/span><\/span>+<\/span><\/span><\/span><\/span>\u03f5<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

          \u7b26\u53f7\u542b\u4e49：<\/p>\n

            \n
          • \n

            Y<\/p>\n

            Y<\/p>\n

            <\/span><\/span>Y<\/span><\/span><\/span><\/span><\/span>：\u5b9e\u9a8c\u6838\u5fc3\u6307\u6807（\u5982\u4eba\u5747\u6d88\u8d39）\u3002<\/li>\n

          • \n

            T<\/p>\n

            T<\/p>\n

            <\/span><\/span>T<\/span><\/span><\/span><\/span><\/span>：\u5e72\u9884\u53d8\u91cf（0 = \u5bf9\u7167\u7ec4，1 = \u5b9e\u9a8c\u7ec4）\u3002<\/li>\n

          • \n

            X<\/p>\n

            X<\/p>\n

            <\/span><\/span>X<\/span><\/span><\/span><\/span><\/span>：\u7528\u6237\u7279\u5f81（\u5982\u6027\u522b，0 = \u5973，1 = \u7537）\u3002<\/li>\n

          • \n

            T<\/p>\n

            \u22c5<\/p>\n

            X<\/p>\n

            T \\\\cdot X<\/p>\n

            <\/span><\/span>T<\/span><\/span>\u22c5<\/span><\/span><\/span><\/span>X<\/span><\/span><\/span><\/span><\/span>：\u4ea4\u4e92\u9879，\u5373\u5e72\u9884\u53d8\u91cf\u4e0e\u7279\u5f81\u7684\u4e58\u79ef\u3002<\/li>\n<\/ul>\n

            \u4ea4\u4e92\u903b\u8f91\u4e0e\u7cfb\u6570\u89e3\u8bfb：<\/p>\n

              \n
            • \n

              \u03b2<\/p>\n

              1<\/p>\n

              \\\\beta_1<\/p>\n

              <\/span><\/span>\u03b2<\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> (\u4e3b\u6548\u5e94)：\u5f53 <\/p>\n

              X<\/p>\n

              =<\/p>\n

              0<\/p>\n

              X=0<\/p>\n

              <\/span><\/span>X<\/span><\/span>=<\/span><\/span><\/span><\/span>0<\/span><\/span><\/span><\/span><\/span>（\u5973\u6027）\u65f6，\u5b9e\u9a8c\u7ec4\u5e26\u6765\u7684\u63d0\u5347\u91cf\u3002<\/li>\n

            • \n

              \u03b2<\/p>\n

              2<\/p>\n

              \\\\beta_2<\/p>\n

              <\/span><\/span>\u03b2<\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> (\u7279\u5f81\u6548\u5e94)：\u5728\u5bf9\u7167\u7ec4\u4e2d，\u7537\u6027\u6bd4\u5973\u6027\u5929\u7136\u9ad8\u51fa\u7684\u6d88\u8d39\u91cf\u3002<\/li>\n

            • \n

              \u03b2<\/p>\n

              3<\/p>\n

              \\\\beta_3<\/p>\n

              <\/span><\/span>\u03b2<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> (\u4ea4\u4e92\u6548\u5e94)：\u8fd9\u662f\u5173\u952e\u3002 \u5b83\u4ee3\u8868\u201c\u7537\u6027\u505a\u5b9e\u9a8c\u7684\u63d0\u5347\u91cf\u201d\u6bd4\u201c\u5973\u6027\u505a\u5b9e\u9a8c\u7684\u63d0\u5347\u91cf\u201d\u591a（\u6216\u5c11）\u4e86\u591a\u5c11\u3002<\/li>\n<\/ul>\n

              \u4e3a\u4ec0\u4e48\u56de\u5f52\u66f4\u597d？<\/p>\n

            • \u7edf\u8ba1\u529f\u6548\u66f4\u9ad8：\u6a21\u578b\u5229\u7528\u5168\u91cf\u6570\u636e\u6765\u4f30\u8ba1\u8bef\u5dee\u9879\u7684\u65b9\u5dee，\u6ca1\u6709\u6d6a\u8d39\u4efb\u4f55\u6837\u672c，\u56e0\u6b64\u80fd\u68c0\u6d4b\u51fa\u66f4\u5fae\u5c0f\u7684\u5dee\u5f02\u3002<\/li>\n
            • \u4e25\u8c28\u6027：\u53ea\u6709\u5f53 \n

              \u03b2<\/p>\n

              3<\/p>\n

              \\\\beta_3<\/p>\n

              <\/span><\/span>\u03b2<\/span><\/span>3<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u7684 P \u503c\u663e\u8457（<0.05），\u4f60\u624d\u6709\u8d44\u683c\u8bf4\u201c\u5b9e\u9a8c\u6548\u679c\u56e0\u4eba\u800c\u5f02\u201d\u3002<\/li>\n

            • \u5904\u7406\u8fde\u7eed\u53d8\u91cf：\u5982\u679c \n

              X<\/p>\n

              X<\/p>\n

              <\/span><\/span>X<\/span><\/span><\/span><\/span><\/span> \u662f\u201c\u6d3b\u8dc3\u5ea6\u5206\u6570\u201d，\u4e0b\u94bb\u5206\u6790\u5fc5\u987b\u4eba\u4e3a\u5206\u6876（\u4e22\u5931\u4fe1\u606f），\u800c\u56de\u5f52\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u201c\u6d3b\u8dc3\u5ea6\u6bcf\u589e\u52a01\u5206，\u5b9e\u9a8c\u6548\u679c\u63d0\u5347\u591a\u5c11\u201d\u3002<\/li>\n

              3.\u5b9e\u6218\u6f14\u7ec3：\u624b\u628a\u624b\u6559\u4f60\u505a\u5f02\u8d28\u6027\u68c0\u9a8c<\/h5>\n

              \u5149\u8bf4\u4e0d\u7ec3\u5047\u628a\u5f0f\u3002\u6211\u4eec\u4ee5\u4e00\u4e2a\u5177\u4f53\u7684**\u201c\u4f1a\u5458\u6298\u6263\u5b9e\u9a8c\u201d**\u4e3a\u4f8b，\u770b\u770b\u5728 Python \u4e2d\u5982\u4f55\u843d\u5730\u3002<\/p>\n

              1. \u573a\u666f\u8bbe\u5b9a\u4e0e\u6570\u636e\u51c6\u5907<\/h6>\n

              \u5047\u8bbe\u6211\u4eec\u8fdb\u884c\u4e86\u4e00\u573a A\/B \u5b9e\u9a8c：<\/p>\n

                \n
              • Y (\u6307\u6807)：\u7528\u6237\u5f53\u5468 GMV\u3002<\/li>\n
              • T (\u5e72\u9884)：\u662f\u5426\u53d1\u653e 8 \u6298\u5238（1=\u5b9e\u9a8c\u7ec4，0=\u5bf9\u7167\u7ec4）\u3002<\/li>\n
              • X (\u7279\u5f81)：\u7528\u6237\u7b49\u7ea7（1=\u9ad8\u6d3b\u7528\u6237，0=\u4f4e\u6d3b\u7528\u6237）\u3002<\/li>\n<\/ul>\n

                \u6211\u4eec\u60f3\u77e5\u9053：\u8fd9\u5f20 8 \u6298\u5238，\u662f\u4e0d\u662f\u5bf9\u9ad8\u6d3b\u7528\u6237\u7684\u523a\u6fc0\u6548\u679c\u66f4\u5927？<\/p>\n

                2. \u4ee3\u7801\u5b9e\u73b0 (Python statsmodels)<\/h6>\n

                \u505a\u7edf\u8ba1\u63a8\u65ad（Inference），\u4e0d\u8981\u7528 sklearn，\u56e0\u4e3a\u5b83\u4e0d\u76f4\u63a5\u63d0\u4f9b P \u503c\u3002\u5de5\u4e1a\u754c\u6807\u51c6\u505a\u6cd5\u662f\u4f7f\u7528 statsmodels\u3002<\/p>\n

                import<\/span> statsmodels.<\/span>formula.<\/span>api as<\/span> smf
                \nimport<\/span> pandas as<\/span> pd<\/p>\n

                # \u5047\u8bbe df \u662f\u4f60\u7684\u6e05\u6d17\u540e\u7684\u6570\u636e DataFrame<\/span>
                \n# df.columns = ['gmv', 'is_treatment', 'is_high_active']<\/span><\/p>\n

                # \u6838\u5fc3\u6b65\u9aa4：\u5b9a\u4e49\u516c\u5f0f<\/span>
                \n# \u7b26\u53f7 '*' \u5728\u516c\u5f0f\u4e2d\u4ee3\u8868：\u4e3b\u6548\u5e94 + \u4ea4\u4e92\u6548\u5e94<\/span>
                \n# \u7b49\u4ef7\u4e8e：gmv ~ is_treatment + is_high_active + is_treatment:is_high_active<\/span>
                \nformula =<\/span> 'gmv ~ is_treatment * is_high_active'<\/span><\/p>\n

                # \u8bad\u7ec3 OLS (\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u6cd5) \u6a21\u578b<\/span>
                \nmodel =<\/span> smf.<\/span>ols(<\/span>formula=<\/span>formula,<\/span> data=<\/span>df)<\/span>.<\/span>fit(<\/span>)<\/span><\/p>\n

                # \u8f93\u51fa\u8be6\u7ec6\u7edf\u8ba1\u62a5\u544a<\/span>
                \nprint<\/span>(<\/span>model.<\/span>summary(<\/span>)<\/span>)<\/span><\/p>\n

                3. \u7ed3\u679c\u89e3\u8bfb（\u5173\u952e！）<\/h6>\n

                \u8fd0\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e，\u4f60\u4f1a\u5f97\u5230\u4e00\u5f20\u590d\u6742\u7684\u8868\u3002\u522b\u614c，\u4f60\u53ea\u9700\u8981\u5173\u6ce8 coef (\u7cfb\u6570) \u548c P>|t| (P\u503c) \u8fd9\u4e24\u5217，\u91cd\u70b9\u770b\u4ee5\u4e0b\u56db\u884c：<\/p>\n\n\u53d8\u91cf\u540d (Variable)Coef (\u7cfb\u6570)P>|t| (\u663e\u8457\u6027)\u4e1a\u52a1\u542b\u4e49\u89e3\u8bfb (\u4eba\u8bdd\u7248)<\/tr>\n\n\n\n\n\n
                Intercept (\u622a\u8ddd)<\/td>\n100.0<\/td>\n0.000<\/td>\n\u57fa\u51c6\u7ebf：\u4f4e\u6d3b\u7528\u6237\u5728\u5bf9\u7167\u7ec4（\u6ca1\u5238）\u5e73\u5747\u4e70 100 \u5757\u3002<\/td>\n<\/tr>\n
                is_treatment<\/td>\n10.0<\/td>\n0.000<\/td>\n\u4f4e\u6d3b\u7528\u6237\u7684 Lift：\u5238\u8ba9\u4f4e\u6d3b\u7528\u6237\u591a\u4e70\u4e86 10 \u5757\u3002<\/td>\n<\/tr>\n
                is_high_active<\/td>\n200.0<\/td>\n0.000<\/td>\n\u5929\u7136\u5dee\u5f02：\u9ad8\u6d3b\u7528\u6237\u672c\u6765\u5c31\u6bd4\u4f4e\u6d3b\u7528\u6237\u591a\u4e70 200 \u5757（\u8ddf\u53d1\u5238\u65e0\u5173）\u3002<\/td>\n<\/tr>\n
                is_treatment:is_high_active<\/td>\n50.0<\/td>\n0.030<\/td>\n\u4ea4\u4e92\u6548\u5e94 (\u91cd\u70b9)：\u9ad8\u6d3b\u7528\u6237\u62ff\u5238\u540e\u7684\u63d0\u5347，\u6bd4\u4f4e\u6d3b\u7528\u6237\u989d\u5916\u591a 50 \u5757\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                4. \u51b3\u7b56\u903b\u8f91<\/h6>\n

                \u770b\u7740\u6700\u540e\u4e00\u884c（\u4ea4\u4e92\u9879）\u7684 P \u503c，\u6309\u4ee5\u4e0b\u903b\u8f91\u4e0b\u7ed3\u8bba：<\/p>\n

                  \n
                • \n

                  \u60c5\u5f62 A：\u4ea4\u4e92\u9879\u663e\u8457 (P < 0.05) \u4e14\u7cfb\u6570 > 0<\/p>\n

                    \n
                  • \u7ed3\u8bba：\u5b9e\u9524\u4e86！\u9ad8\u6d3b\u7528\u6237\u5bf9\u5238\u66f4\u654f\u611f（Lift = 10 + 50 = 60\u5757），\u4f4e\u6d3b\u7528\u6237\u4e0d\u592a\u654f\u611f（Lift = 10\u5757）\u3002<\/li>\n
                  • \u52a8\u4f5c：\u5982\u679c\u9884\u7b97\u6709\u9650，\u4f18\u5148\u7ed9\u9ad8\u6d3b\u7528\u6237\u53d1\u5238，ROI \u6700\u9ad8\u3002<\/li>\n<\/ul>\n<\/li>\n
                  • \n

                    \u60c5\u5f62 B：\u4ea4\u4e92\u9879\u663e\u8457 (P < 0.05) \u4e14\u7cfb\u6570 < 0<\/p>\n

                      \n
                    • \u7ed3\u8bba：\u53cd\u8f6c\u4e86！\u9ad8\u6d3b\u7528\u6237\u53cd\u800c\u6ca1\u90a3\u4e48\u53d7\u7528（\u751a\u81f3\u53ef\u80fd\u4ea7\u751f\u53cd\u611f）\u3002<\/li>\n
                    • \u52a8\u4f5c：\u68c0\u67e5\u7b56\u7565\u662f\u5426\u5bf9\u9ad8\u6d3b\u7528\u6237\u6709\u5e72\u6270，\u6216\u8005\u53ea\u5bf9\u4f4e\u6d3b\u7528\u6237\u4e0a\u7ebf\u3002<\/li>\n<\/ul>\n<\/li>\n
                    • \n

                      \u60c5\u5f62 C：\u4ea4\u4e92\u9879\u4e0d\u663e\u8457 (P > 0.05)<\/p>\n

                        \n
                      • \u7ed3\u8bba：\u4f17\u751f\u5e73\u7b49\u3002\u867d\u7136\u8089\u773c\u770b\u7cfb\u6570\u6709\u5dee\u5f02（\u6bd4\u5982\u7b97\u51fa\u6765\u662f 5.0），\u4f46\u7edf\u8ba1\u4e0a\u8fd9\u5c5e\u4e8e\u968f\u673a\u8bef\u5dee\u3002<\/li>\n
                      • \u52a8\u4f5c：\u4e0d\u8981\u641e\u533a\u522b\u5bf9\u5f85，\u5168\u91cf\u4e0a\u7ebf\u5373\u53ef，\u7ef4\u62a4\u4e00\u5957\u7b56\u7565\u6210\u672c\u6700\u4f4e\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                        \u901a\u8fc7\u8fd9\u5957\u6d41\u7a0b，\u4f60\u5c31\u719f\u6e05\u695a\u4e86\u5982\u4f55\u5b9e\u6253\u5b9e\u6267\u884c\u7ebf\u6027\u56de\u5f52\u3002<\/p>\n

                        \u56db\u3001\u8fdb\u9636\u5f15\u7533：\u4ece\u56de\u5f52\u5230 CUPED \u4e0e Uplift<\/h4>\n

                        \u7406\u89e3\u4e86\u56de\u5f52\u5728 A\/B \u5b9e\u9a8c\u4e2d\u7684\u672c\u8d28，\u4f60\u5c31\u80fd\u4e32\u8054\u8d77\u66f4\u591a\u9ad8\u9636\u6982\u5ff5\u3002<\/p>\n

                        1. CUPED \u672c\u8d28\u5c31\u662f ANCOVA<\/h5>\n

                        \u5de5\u4e1a\u754c\u5e38\u7528\u7684\u65b9\u5dee\u7f29\u51cf\u5927\u6cd5 CUPED (Controlled-experiment Using Pre-Experiment Data)，\u5176\u6570\u5b66\u672c\u8d28\u4e0e\u534f\u65b9\u5dee\u5206\u6790 (ANCOVA) \u662f\u4e00\u81f4\u7684\u3002 \u5982\u679c\u4f60\u5728\u56de\u5f52\u65b9\u7a0b\u4e2d\u53bb\u6389\u4ea4\u4e92\u9879，\u53ea\u4fdd\u7559\u534f\u53d8\u91cf： <\/p>\n

                        Y<\/p>\n

                        =<\/p>\n

                        \u03b2<\/p>\n

                        0<\/p>\n

                        +<\/p>\n

                        \u03b2<\/p>\n

                        1<\/p>\n

                        T<\/p>\n

                        +<\/p>\n

                        \u03b2<\/p>\n

                        2<\/p>\n

                        X<\/p>\n

                        p<\/p>\n

                        r<\/p>\n

                        e<\/p>\n

                        +<\/p>\n

                        \u03f5<\/p>\n

                        Y = \\\\beta_0 + \\\\beta_1 T + \\\\beta_2 X_{pre} + \\\\epsilon <\/p>\n

                        <\/span><\/span>Y<\/span><\/span>=<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>0<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>T<\/span><\/span>+<\/span><\/span><\/span><\/span>\u03b2<\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>X<\/span><\/span>p<\/span>re<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>\u03f5<\/span><\/span><\/span><\/span><\/span><\/span> \u8fd9\u91cc\u7684 <\/p>\n

                        X<\/p>\n

                        p<\/p>\n

                        r<\/p>\n

                        e<\/p>\n

                        X_{pre}<\/p>\n

                        <\/span><\/span>X<\/span><\/span>p<\/span>re<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f\u5b9e\u9a8c\u524d\u7684\u6570\u636e\u3002\u6b64\u65f6 <\/p>\n

                        \u03b2<\/p>\n

                        1<\/p>\n

                        \\\\beta_1<\/p>\n

                        <\/span><\/span>\u03b2<\/span><\/span>1<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u7684\u4f30\u8ba1\u65b9\u5dee\u4f1a\u5927\u5e45\u964d\u4f4e\u3002\u6240\u4ee5，\u8dd1\u4e00\u4e2a\u5e26\u5b9e\u9a8c\u524d\u7279\u5f81\u7684\u7ebf\u6027\u56de\u5f52，\u5c31\u662f\u5728\u505a\u65b9\u5dee\u7f29\u51cf\u3002<\/p>\n

                        2. Uplift Modeling \u7684\u201c\u624b\u52a8\u6321\u201d<\/h5>\n

                        Uplift Modeling (\u589e\u76ca\u6a21\u578b) \u65e8\u5728\u9884\u6d4b\u6bcf\u4e2a\u7528\u6237\u7684\u4e2a\u4f53\u56e0\u679c\u6548\u5e94 (CATE)\u3002 \u6211\u4eec\u5728\u4e0a\u9762\u8bb2\u7684\u201c\u7ebf\u6027\u56de\u5f52 + \u4ea4\u4e92\u9879\u201d，\u5176\u5b9e\u5c31\u662f\u6700\u57fa\u7840\u7684 Uplift \u6a21\u578b（\u5b66\u672f\u4e0a\u79f0\u4e3a S-Learner \u7684\u7ebf\u6027\u7248\u672c）\u3002<\/p>\n

                          \n
                        • \u7ebf\u6027\u56de\u5f52：\u9700\u8981\u4f60\u624b\u52a8\u6307\u5b9a\u4ea4\u4e92\u9879（\u6bd4\u5982\u624b\u52a8\u4e58\u4e0a\u6027\u522b\u3001\u57ce\u5e02），\u5c5e\u4e8e\u201c\u624b\u52a8\u6321\u201d\u3002<\/li>\n
                        • \u6811\u6a21\u578b Uplift：\u5229\u7528\u968f\u673a\u68ee\u6797\u6216 XGBoost \u81ea\u52a8\u5bfb\u627e\u7279\u5f81\u4e0e\u5e72\u9884\u7684\u4ea4\u4e92\u89c4\u5219，\u5c5e\u4e8e\u201c\u81ea\u52a8\u6321\u201d\u3002<\/li>\n<\/ul>\n

                          \u4e94\u3001\u603b\u7ed3<\/h4>\n

                          \u4e0d\u8981\u56e0\u4e3a\u7ebf\u6027\u56de\u5f52\u201c\u7b80\u5355\u201d\u5c31\u8f7b\u89c6\u5b83\u3002\u5728 A\/B \u5b9e\u9a8c\u4e2d：<\/p>\n

                        • T \u68c0\u9a8c\u662f\u5165\u95e8\u5de5\u5177，\u9002\u5408\u770b\u6574\u4f53\u6982\u51b5\u3002<\/li>\n
                        • \u4e0b\u94bb\u5206\u6790\u662f\u76f4\u89c2\u5de5\u5177，\u4f46\u5bb9\u6613\u6389\u5165\u7edf\u8ba1\u9677\u9631\u3002<\/li>\n
                        • \u7ebf\u6027\u56de\u5f52\u662f\u9ad8\u9636\u5229\u5668，\u901a\u8fc7\u4ea4\u4e92\u9879，\u5b83\u80fd\u4ee5\u6700\u9ad8\u7684\u7edf\u8ba1\u529f\u6548\u548c\u4e25\u8c28\u6027，\u544a\u8bc9\u4f60\u5b9e\u9a8c\u6548\u679c\u5230\u5e95\u662f\u5426\u5b58\u5728\u4eba\u7fa4\u5dee\u5f02\u3002<\/li>\n

                          \u4e0b\u6b21\u5f53\u4e1a\u52a1\u65b9\u95ee\u4f60\u201c\u4e3a\u4ec0\u4e48\u8fd9\u7ec4\u6da8\u4e86\u90a3\u7ec4\u6ca1\u6da8\u201d\u65f6，\u522b\u6025\u7740\u62c9 Excel \u5207\u7247，\u8bd5\u7740\u8dd1\u4e00\u4e2a\u5e26\u4ea4\u4e92\u9879\u7684\u56de\u5f52\u6a21\u578b，\u7528\u4ea4\u4e92\u9879\u7684 P \u503c\u8bf4\u8bdd\u3002<\/p>\n

                          \n

                          \u5982\u679c\u8fd9\u7bc7\u6587\u7ae0\u5e2e\u4f60\u7406\u6e05\u4e86\u601d\u8def，\u4e0d\u59a8\u70b9\u4e2a\u5173\u6ce8，\u6211\u4f1a\u6301\u7eed\u5206\u4eab AB \u5b9e\u9a8c\u5e72\u8d27\u6587\u7ae0\u3002 \"\u6c42\u70b9\u8d5e\u5173\u6ce8\"<\/p>\n","protected":false},"excerpt":{"rendered":"

                          \u2014\u5173\u6ce8\u4f5c\u8005，\u9001A\/B\u5b9e\u9a8c\u5b9e\u6218\u5de5\u5177\u5305 \u5f88\u591a\u521a\u5165\u884c\u7684\u6570\u636e\u5206\u6790\u5e08\u6216\u5de5\u7a0b\u5e08，\u5bf9 A\/B \u5b9e\u9a8c\u7684\u7406\u89e3\u5f80\u5f80\u505c\u7559\u5728 T \u68c0\u9a8c（T-test）\u7684\u5c42\u9762：\u628a\u6d41\u91cf\u5206\u4e24\u534a，\u7b97\u7b97\u5747\u503c，\u770b\u770b P \u503c，\u7ed3\u675f\u3002
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