{"id":21396,"date":"2020-10-22T19:09:36","date_gmt":"2020-10-22T11:09:36","guid":{"rendered":"https:\/\/swarma.org\/?p=21396"},"modified":"2020-10-22T19:09:36","modified_gmt":"2020-10-22T11:09:36","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8%e8%a7%82%e6%b5%8b%e6%95%b0%e6%8d%ae%e4%b8%8b%e8%bf%9b%e8%a1%8c%e5%9b%a0%e6%9e%9c%e6%95%88%e5%ba%94%e8%af%84%e4%bc%b0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=21396","title":{"rendered":"\u5982\u4f55\u5728\u89c2\u6d4b\u6570\u636e\u4e0b\u8fdb\u884c\u56e0\u679c\u6548\u5e94\u8bc4\u4f30"},"content":{"rendered":"
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\u4ec0\u4e48\u662f\u56e0\u679c\uff1f\u56e0\u679c\u6027\u4e0e\u76f8\u5173\u6027\u7684\u533a\u522b\u662f\u4ec0\u4e48\uff1f\u76f8\u5173\u6027\u6709\u54ea\u51e0\u79cd\u6765\u6e90\uff1f\u5982\u4f55\u8bc4\u4f30\u56e0\u679c\u6548\u5e94\uff1f\u57282020\u5e74\u96c6\u667a-\u51ef\u98ce\u7814\u8bfb\u8425\u4e2d\uff0c<\/span>\u6d59\u6c5f\u5927\u5b66\u8ba1\u7b97\u673a\u5b66\u9662\u52a9\u7406\u6559\u6388<\/span>\u51b5\u7428\u5c31\u4e0a\u8ff0\u95ee\u9898\u505a\u4e86\u7cfb\u7edf\u68b3\u7406\uff0c\u672c\u7bc7\u662f\u6587\u5b57\u6574\u7406\u7248\u3002<\/span><\/section>\n

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\u81ea9\u670820\u65e5\uff08\u5468\u65e5\uff09\u5f00\u59cb\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u5317\u4eac\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u8fd8\u5c06\u4e3e\u884c\u4e00\u7cfb\u5217\u6709\u5173\u56e0\u679c\u63a8\u7406\u7684\u8bfb\u4e66\u4f1a\uff0c\u6b22\u8fce\u66f4\u591a\u7684\u6709\u5174\u8da3\u7684\u540c\u5b66\u548c\u76f8\u5173\u7814\u7a76\u8005\u53c2\u52a0\uff0c\u4e00\u8d77\u8fce\u63a5\u56e0\u679c\u79d1\u5b66\u7684\u65b0\u65f6\u4ee3\u3002<\/span><\/span><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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1. \u56e0\u679c\u6027\u4e0e\u76f8\u5173\u6027<\/strong><\/span><\/p>\n

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1.1 \u4ec0\u4e48\u662f\u56e0\u679c<\/span><\/strong><\/span><\/section>\n<\/h2>\n
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\u4ec0\u4e48\u662f\u56e0\u679c\u5462\uff1f\u201c\u56e0\u201d\u5176\u5b9e\u5c31\u662f\u5f15\u8d77\u67d0\u79cd\u73b0\u8c61\u53d1\u751f\u7684\u539f\u56e0\uff0c\u800c\u201c\u679c\u201d\u5c31\u662f\u67d0\u79cd\u73b0\u8c61\u53d1\u751f\u540e\u4ea7\u751f\u7684\u7ed3\u679c\u3002\u56e0\u679c\u95ee\u9898\u5728\u6211\u4eec\u65e5\u5e38\u751f\u6d3b\u4e2d\u5341\u5206\u5e38\u89c1\u3002<\/span><\/section>\n

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\u9996\u5148\u5728\u533b\u7597\u65b9\u9762\u3002\u6bd4\u5982\u5728\u8fd9\u6b21\u65b0\u51a0\u75ab\u60c5\u4e2d\uff0c\u5404\u4e2a\u56fd\u5bb6\u90fd\u5728\u4e89\u5148\u6050\u540e\u5730\u7814\u53d1\u75ab\u82d7\uff0c\u4f46\u5728\u75ab\u82d7\u4e0a\u5e02\u4e4b\u524d\u8fd8\u9700\u8981\u505a\u5f88\u591a\u6b21\u5355\u76f2\u5b9e\u9a8c\u3001\u53cc\u76f2\u5b9e\u9a8c\uff0c\u5176\u80cc\u540e\u5c31\u662f\u57fa\u4e8e\u968f\u673a\u5bf9\u7167\u5b9e\u9a8c\u7684\u4e00\u4e9b\u56e0\u679c\u63a8\u7406\u548c\u56e0\u679c\u6548\u5e94\u8bc4\u4f30\uff0c\u786e\u5b9a\u836f\u7269\u4e8e\u75c5\u4eba\u5eb7\u590d\u4e4b\u95f4\u7684\u56e0\u679c\u6548\u5e94\u3002<\/span><\/section>\n

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\u5176\u6b21\u5728\u793e\u4f1a\u79d1\u5b66\u65b9\u9762\u3002\u6bd4\u5982\u5728\u56fd\u5bb6\u51fa\u53f0\u65b0\u7684\u653f\u7b56\u524d\uff0c\u5c31\u8981\u5229\u7528\u56e0\u679c\u63a8\u7406\u624b\u6bb5\u6765\u4f30\u8ba1\u8fd9\u4e2a\u653f\u7b56\u4f1a\u7ed9\u6c11\u4f17\u3001\u7ecf\u6d4e\u6548\u5e94\u548c\u793e\u4f1a\u6548\u5e94\u5e26\u6765\u591a\u5927\u5f71\u54cd\u3002<\/span><\/section>\n

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\u6700\u540e\u5728\u5e02\u573a\u8425\u9500\u65b9\u9762\u3002\u6bd4\u5982\u5728\u63a8\u5e7f\u544a\u4e4b\u524d\uff0c\u5c31\u9700\u8981\u4f7f\u7528\u968f\u673a\u5bf9\u7167\u8bd5\u9a8c\u6216\u8005A\/B\u6d4b\u8bd5\uff0c\u9009\u62e9\u5e7f\u544a\u63a8\u9001\u7684\u7b56\u7565\u4ee5\u5b9e\u73b0\u6548\u76ca\u6700\u5927\u5316\u3002<\/span><\/section>\n

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1.2 \u56e0\u679c\u6027\u4e0e\u76f8\u5173\u6027\u7684\u533a\u522b<\/span><\/strong><\/span><\/section>\n<\/h2>\n
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\u76f8\u5173\u6027\u6bd4\u56e0\u679c\u6027\u66f4\u7f3a\u4e4f\u53ef\u89e3\u91ca\u6027<\/strong><\/span>\uff08Explainability\uff09<\/span>\u3002\u8fd9\u5f20\u56fe\u4e2d\uff0c\u9ed1\u7ebf\u662f\u80af\u5854\u57fa\u5dde\u7684\u7ed3\u5a5a\u7387\uff0c\u800c\u7ea2\u7ebf\u662f\u6e14\u8239\u4e8b\u6545\u6b7b\u4ea1\u4eba\u6570\uff0c\u4e24\u8005\u5177\u6709\u5f88\u9ad8\u7684\u76f8\u5173\u6027\uff0c\u4f46\u4e24\u8005\u4e4b\u95f4\u5374\u6ca1\u6709\u4efb\u4f55\u56e0\u679c\u5173\u7cfb\u3002\u6211\u4eec\u5728\u4f7f\u7528\u6570\u636e\u7684\u65f6\u5019\u5c31\u9700\u8981\u77e5\u9053\uff0c\u8fd9\u91cc\u7684\u76f8\u5173\u6027\u662f\u4e0d\u53ef\u9760\u7684\u3001\u4e0d\u53ef\u89e3\u91ca\u7684\u3002<\/span><\/section>\n
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\u53e6\u5916\u4e00\u4e2a\u4f8b\u5b50\u662f\u592a\u9633\u955c\u4e0e\u51b0\u6dc7\u6dcb\u7684\u9500\u552e\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4e24\u8005\u4e4b\u95f4\u5448\u73b0\u7740\u660e\u663e\u7684\u6b63\u76f8\u5173\u6027\u3002\u4f46\u5982\u679c\u6211\u4eec\u76f4\u63a5\u5173\u95ed\u592a\u9633\u955c\u5546\u5e97\uff0c\u8fdb\u884c\u5e72\u9884\uff0c\u4f1a\u5f71\u54cd\u51b0\u6dc7\u6dcb\u7684\u9500\u91cf\u5417\uff1f\u5e76\u4e0d\u4f1a\u3002\u56e0\u4e3a\u4e24\u8005\u4e4b\u95f4\u7684\u865a\u5047\u76f8\u5173\u6027\u662f\u7531\u5929\u6c14\u5f15\u53d1\u7684\uff0c\u5728\u592a\u9633\u708e\u70ed\u65f6\u4e24\u8005\u7684\u6d88\u8d39\u91cf\u90fd\u4f1a\u63d0\u5347\uff0c\u5f3a\u5236\u5e72\u9884\u5176\u4e2d\u4e00\u4e2a\u7684\u9500\u91cf\u5e76\u4e0d\u4f1a\u76f4\u63a5\u5f71\u54cd\u53e6\u4e00\u4e2a\u3002<\/span><\/section>\n
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\u76f8\u5173\u6027\u6bd4\u56e0\u679c\u6027\u66f4\u7f3a\u4e4f\u7a33\u5b9a\u6027<\/strong><\/span>\uff08Stability\uff09<\/span>\u3002\u6bd4\u5982\u6211\u4eec\u8bad\u7ec3\u6a21\u578b\u53bb\u8bc6\u522b\u56fe\u7247\u4e2d\u7684\u72d7\uff0c\u4f46\u6570\u636e\u96c6\u4e2d90%\u7684\u72d7\u90fd\u662f\u5728\u8349\u5730\u4e0a\u7684\uff0c\u90a3\u4e48\u5728\u8fd9\u4e2a\u6570\u636e\u96c6\u4e2d\u8349\u5730\u4e0e\u72d7\u5c31\u5341\u5206\u76f8\u5173\u3002\u90a3\u4e48\u5982\u679c\u6211\u4eec\u5229\u7528\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\uff0c\u65e0\u8bba\u662f\u903b\u8f91\u65af\u8482\u56de\u5f52\u8fd8\u662f\u6df1\u5ea6\u6a21\u578b\uff0c\u5927\u6982\u7387\u4f1a\u628a\u8349\u5730\u8bc6\u522b\u4e3a\u91cd\u8981\u7684\u7279\u5f81\u3002\u4f46\u5982\u679c\u6d4b\u8bd5\u6570\u636e\u96c6\u4e2d\u7684\u72d7\u662f\u5728\u6c99\u6ee9\u4e0a\u6216\u8005\u6c34\u4e2d\uff0c\u6a21\u578b\u5c31\u6709\u5f88\u5927\u6982\u7387\u4f1a\u5931\u8d25\u3002<\/span><\/section>\n

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\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u4f7f\u57fa\u4e8e\u5173\u8054\u9a71\u52a8\u7684\uff0c\u5bf9\u4e8e\u672a\u77e5\u7684\u6d4b\u8bd5\u6570\u636e\u96c6\u5f88\u96be\u8fbe\u5230\u7a33\u5b9a\u9884\u6d4b\u3002\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u5728\u5173\u8054\u6316\u6398\u4e2d\u4f1a\u53d1\u73b0\u4e00\u4e9b\u975e\u56e0\u679c\u7279\u5f81\uff0c\u6bd4\u5982\u8349\u5730\u80cc\u666f\u4e0e\u6807\u7b7e\u7684\u5173\u7cfb\uff0c\u5e76\u5229\u7528\u8fd9\u79cd\u5f3a\u7684\u865a\u5047\u76f8\u5173<\/span>\uff08Spurious Correlation\uff09<\/span>\u8fdb\u884c\u9884\u6d4b\u3002\u5982\u679c\u6211\u4eec\u80fd\u591f\u53d1\u73b0\u7279\u5f81\u4e0e\u6807\u7b7e\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u6bd4\u5982\u6211\u4eec\u4eba\u7c7b\u5728\u8bc6\u522b\u72d7\u7684\u65f6\u5019\u5c31\u4f1a\u53bb\u5173\u6ce8\u591f\u7684\u9f3b\u5b50\u3001\u773c\u775b\u548c\u8033\u6735\u8fd9\u4e9b\u56e0\u679c\u7279\u5f81\uff0c\u90a3\u4e48\u65e0\u8bba\u72d7\u662f\u5728\u4ec0\u4e48\u80cc\u666f\u4e0b\uff0c\u6211\u4eec\u90fd\u53ef\u4ee5\u6b63\u786e\u8bc6\u522b\u3002<\/span><\/section>\n
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\u7b2c\u4e09\u4e2a\u533a\u522b\u662f\u53ef\u884c\u52a8\u6027<\/strong><\/span>\uff08Actionability\uff09<\/span>\u3002\u6bd4\u5982\u67d0\u4e2a\u7535\u5546\u5728\u63a8\u5e7f\u67d0\u4e2a\u5546\u54c1\u65f6\uff0c\u9700\u8981\u4ece\u4e24\u4e2a\u5e7f\u544a\u63a8\u8350\u7b97\u6cd5\u4e2d\u8fdb\u884c\u9009\u62e9\uff0c\u770b\u54ea\u4e2a\u7b97\u6cd5\u80fd\u5e26\u6765\u7684\u6536\u76ca\u66f4\u5927\u3002<\/span><\/section>\n

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\u5982\u56fe\u6240\u793a\uff0c\u524d\u671f\u7684\u8bd5\u9a8c\u53d1\u73b0\uff0c\u65b0\u7b97\u6cd5B\u6bd4\u65e7\u7b97\u6cd5 A \u7684\u603b\u4f53\u6210\u529f\u7387\u66f4\u9ad8\u3002\u4f46\u5982\u679c\u5c06\u7528\u6237\u6309\u6536\u5165\u5206\u4e3a\u4e24\u5c42\uff0c\u5374\u4f1a\u53d1\u73b0\u7b97\u6cd5A\u5728\u4f4e\u6536\u5165\u4eba\u7fa4\u548c\u9ad8\u6536\u5165\u4eba\u7fa4\u4e2d\u7684\u6548\u679c\u53cd\u800c\u90fd\u4f18\u4e8e B \u3002\u4e24\u4e2a\u7b97\u6cd5\u7684\u8bd5\u9a8c\u5bf9\u8c61\u4e2d\uff0c\u6536\u5165\u5206\u5e03\u7684\u5dee\u522b\u5f88\u5927\uff0c\u5982\u679c\u4e0d\u8fdb\u884c\u63a7\u5236\uff0c\u5c31\u4f1a\u4ea7\u751f\u9519\u8bef\u7684\u7ed3\u679c\u3002\u800c\u9664\u4e86\u6536\u5165\u4e4b\u5916\uff0c\u53ef\u80fd\u8fd8\u9700\u8981\u8003\u8651\u5730\u57df\u3001\u5e74\u9f84\u7b49\u591a\u4e2a\u53d8\u91cf\uff0c\u5426\u5219\u5c31\u4f1a\u4ea7\u751f\u7b97\u6cd5\u4e0e\u6210\u529f\u7387\u4e4b\u95f4\u7684\u865a\u5047\u76f8\u5173\u3002<\/span><\/section>\n

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\u8fd9\u4e9b\u865a\u5047\u76f8\u5173\u662f\u7531\u6df7\u6dc6\u53d8\u91cf\u4ea7\u751f\u7684\u6df7\u6742\u504f\u501a<\/span>\uff08Confounding Bias\uff09<\/span>\u3002\u8fd9\u79cd\u51b3\u7b56\u95ee\u9898\u5b9e\u9645\u4e0a\u662f\u53cd\u4e8b\u5b9e\u95ee\u9898\uff0c\u800c\u4e0d\u662f\u9884\u6d4b\u95ee\u9898\u3002<\/span><\/section>\n
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\u7b2c\u56db\u4e2a\u533a\u522b\u662f\u516c\u5e73\u6027<\/strong><\/span>\uff08Fairness\uff09<\/span>\u3002Google\u66fe\u5f00\u53d1\u4e86\u6839\u636e\u4eba\u50cf\u5224\u65ad\u72af\u7f6a\u7387\u7684\u8f6f\u4ef6\uff0c\u8f93\u5165\u4e3a\u9ed1\u4eba\u65f6\u72af\u7f6a\u7387\u5c31\u4f1a\u6bd4\u767d\u4eba\u66f4\u9ad8\u3002\u800c\u80a4\u8272\u4e0e\u72af\u7f6a\u7387\u4e4b\u95f4\u4e0d\u5e94\u8be5\u5b58\u5728\u56e0\u679c\u5173\u7cfb\uff0c\u8fd9\u5c31\u51fa\u73b0\u4e86\u516c\u5e73\u6027\u7684\u95ee\u9898\u3002\u5b9e\u9645\u4e0a\u5982\u56fe\u6240\u793a\uff0c\u771f\u6b63\u8d77\u51b3\u5b9a\u6027\u4f5c\u7528\u7684\u53d8\u91cf\u662f\u201c\u6536\u5165\u201d\uff0c\u9ed1\u4eba\u7684\u6536\u5165\u666e\u904d\u504f\u4f4e\uff0c\u800c\u4f4e\u6536\u5165\u4eba\u7fa4\u7684\u72af\u7f6a\u7387\u8f83\u9ad8\uff0c\u56e0\u6b64\u80a4\u8272\u548c\u72af\u7f6a\u7387\u4e4b\u95f4\u51fa\u73b0\u4e86\u865a\u5047\u76f8\u5173\u3002<\/span><\/section>\n

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\u800c\u901a\u8fc7\u56e0\u679c\u8bc4\u4f30\u7684\u6846\u67b6\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528Do-\u6f14\u7b97<\/span>\uff08Do-Calculus\uff09<\/span>\u7b49\u5de5\u5177\uff0c\u5e72\u9884\u6536\u5165\u7684\u591a\u5c11\uff0c\u6765\u8ba1\u7b97\u80a4\u8272\u4e0e\u72af\u7f6a\u7387\u4e4b\u95f4\u771f\u6b63\u7684\u56e0\u679c\u6548\u5e94\u5927\u5c0f\u3002\u5b9e\u9645\u4e0a\uff0c\u6536\u5165\u548c\u72af\u7f6a\u7387\u624d\u662f\u5f3a\u56e0\u679c\u76f8\u5173\u7684\uff0c\u800c\u80a4\u8272\u548c\u72af\u7f6a\u7387\u4e4b\u95f4\u56e0\u679c\u6548\u5e94\u53ef\u4ee5\u5f31\u5230\u5ffd\u7565\u4e0d\u8ba1\u3002<\/span><\/section>\n

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1.3 \u76f8\u5173\u6027\u7684\u4e09\u79cd\u6765\u6e90<\/span><\/strong><\/span><\/section>\n<\/h2>\n
       <\/span>      <\/span><\/section>\n
\u76f8\u5173\u6027\u6709\u4e09\u79cd\u6765\u6e90\uff1a\u56e0\u679c\u3001\u6df7\u6dc6\u548c\u6837\u672c\u9009\u62e9\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u56e0\u679c\u5173\u8054\u4f8b\u5b50\u5c31\u662f\u5929\u4e0b\u96e8\u5730\u9762\u4f1a\u6e7f\uff0c\u8fd9\u79cd\u5173\u7cfb\u662f\u80fd\u591f\u88ab\u4eba\u7c7b\u6240\u7406\u89e3\u7684\u3001\u662f\u53ef\u89e3\u91ca\u7684\u3001\u7a33\u5b9a\u7684<\/span>\uff08\u65e0\u8bba\u5728\u4efb\u4f55\u56fd\u5bb6\u6216\u57ce\u5e02\uff0c\u5929\u4e0b\u96e8\u5730\u90fd\u4f1a\u6e7f\uff09<\/span>\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u6df7\u6dc6\u5173\u8054\u662f\u7531\u6df7\u6dc6\u504f\u5dee<\/span>\uff08Confounding Bias\uff09<\/span>\u9020\u6210\u7684\u3002\u6bd4\u5982\u56fe\u4e2dX\u662fT\u548cY\u7684\u5171\u540c\u539f\u56e0\uff0c\u4f46\u5982\u679c\u4e0d\u5bf9X\u8fdb\u884c\u89c2\u5bdf\uff0c\u5c31\u4f1a\u53d1\u73b0T\u548cY\u662f\u5177\u6709\u76f8\u5173\u6027\u7684\uff0c\u4f46T\u548cY\u4e4b\u95f4\u662f\u6ca1\u6709\u76f4\u63a5\u56e0\u679c\u6548\u5e94\u7684\uff0c\u8fd9\u5c31\u662f\u4ea7\u751f\u4e86\u865a\u5047\u76f8\u5173\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u6837\u672c\u9009\u62e9\u504f\u5dee<\/span>\uff08Selection Bias\uff09<\/span>\u4e5f\u4f1a\u4ea7\u751f\u76f8\u5173\u6027\uff0c\u6bd4\u5982\u4e4b\u524d\u7684\u4f8b\u5b50\u4e2d\uff0c\u5982\u679c\u6570\u636e\u96c6\u4e2d\u7684\u72d7\u90fd\u51fa\u73b0\u5728\u6c99\u6ee9\u4e0a\uff0c\u800c\u6ca1\u6709\u72d7\u7684\u56fe\u7247\u90fd\u662f\u8349\u5730\uff0c\u90a3\u4e48\u8bad\u7ec3\u5904\u7684\u6a21\u578b\u5c31\u4f1a\u53d1\u73b0\u8349\u5730\u4e0e\u72d7\u4e4b\u95f4\u662f\u8d1f\u76f8\u5173\u7684\uff0c\u8fd9\u4e5f\u4ea7\u751f\u4e86\u865a\u5047\u76f8\u5173\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u865a\u5047\u76f8\u5173\u4e0e\u56e0\u679c\u5173\u8054\u76f8\u6bd4\uff0c\u7f3a\u4e4f\u53ef\u89e3\u91ca\u6027\uff0c\u4e14\u5bb9\u6613\u968f\u7740\u73af\u5883\u53d8\u5316\u3002\u5728\u5de5\u4e1a\u754c\u548c\u5b66\u672f\u754c\u4e2d\uff0c\u6211\u4eec\u90fd\u5e0c\u671b\u80fd\u5224\u65ad\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7a76\u7adf\u662f\u56e0\u679c\u5173\u8054\u8fd8\u662f\u865a\u5047\u76f8\u5173\u3002\u5982\u679c\u662f\u865a\u5047\u76f8\u5173\u7684\u8bdd\uff0c\u53ef\u80fd\u4f1a\u7ed9\u5b9e\u9645\u7684\u7cfb\u7edf\u5e26\u6765\u98ce\u9669\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u6240\u4ee5\u8bf4\uff0c\u6062\u590d\u56e0\u679c\u53ef\u4ee5\u63d0\u9ad8\u53ef\u89e3\u91ca\u6027\uff0c\u5e2e\u52a9\u6211\u4eec\u505a\u51fa\u51b3\u7b56\uff0c\u5e76\u5728\u672a\u6765\u7684\u6570\u636e\u96c6\u4e2d\u505a\u51fa\u7a33\u5b9a\u800c\u9c81\u68d2\u7684\u9884\u6d4b\uff0c\u9632\u6b62\u7b97\u6cd5\u4ea7\u751f\u7684\u504f\u5dee\u3002\u65e0\u8bba\u6570\u636e\u96c6\u4e2d\u6709\u4ec0\u4e48\u6837\u7684\u504f\u5dee\uff0c\u6211\u4eec\u90fd\u5e0c\u671b\u80fd\u6316\u6398\u51fa\u6ca1\u6709\u504f\u5dee\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u6765\u6307\u5bfc\u7b97\u6cd5\u3002<\/span><\/section>\n

<\/span><\/section>\n

\n
1.4 \u7b26\u53f7\u5b9a\u4e49<\/span><\/strong><\/span><\/section>\n<\/h2>\n
 <\/span>       <\/span>       <\/span><\/section>\n
\u8fd9\u91cc\u7ed9\u51fa\u5173\u4e8e\u56e0\u679c\u7684\u4e00\u4e2a\u6bd4\u8f83\u5b9e\u9645\u7684\u5b9a\u4e49\uff1a\u53d8\u91cf T \u7684\u53d8\u91cf Y \u7684\u539f\u56e0\uff0c\u53d8\u91cf Y \u662f\u53d8\u91cf T \u7684\u7ed3\u679c\uff0c\u5f53\u4e14\u4ec5\u5f53\u5728\u63a7\u5236\u5176\u4ed6\u6240\u6709\u53d8\u91cf\u4e0d\u53d8\u65f6\uff0c\u6539\u53d8 T \u4f1a\u5f15\u53d1 Y \u7684\u53d8\u5316\u3002\u800c\u56e0\u679c\u6548\u5e94<\/span>\uff08Causal Effect\uff09<\/span>\u5c31\u662f\u6539\u53d8\u53d8\u91cf T \u4e00\u4e2a\u5355\u4f4d\u65f6\uff0c\u53d8\u91cf Y \u53d1\u751f\u6539\u53d8\u7684\u5927\u5c0f\u3002\u8fd9\u91cc\u7684\u4e24\u4e2a\u91cd\u70b9\u662f\uff1a\u4e00\u3001\u53ea\u4fee\u6539 T \u7684\u503c\uff0c\u4e8c\u3001\u4fdd\u6301\u5176\u4ed6\u53d8\u91cf\u4e0d\u53d8\u3002<\/span><\/section>\n
 <\/span>      <\/span><\/section>\n

<\/section>\n
\u8fd9\u91cc\u7ed9\u51fa\u56e0\u679c\u6548\u5e94\u8bc4\u4f30<\/span>\uff08Causal Effect Estimation\uff09<\/span>\u7684\u6570\u5b66\u5f62\u5f0f\u3002\u4ee5\u8bc4\u4f30\u836f\u7269\u7684\u56e0\u679c\u6548\u5e94\u4e3a\u4f8b\uff0c\u5e72\u9884\u53d8\u91cf<\/span>\uff08Treatment Variable\uff09<\/span>T \u7684\u503c\u4e3a1\u65f6\u4ee3\u8868\u5403\u4e86\u836f\uff0c0\u4ee3\u8868\u6ca1\u5403\u836f\uff0c\u800c\u8fd9\u5bf9\u5e94\u4e86\u4e24\u8005\u4e0d\u540c\u7684\u6f5c\u5728\u7ed3\u679c\u3002\u5e73\u5747\u56e0\u679c\u6548\u5e94<\/span>\uff08Average Causal Effect, ATE\uff09<\/span>\u5c31\u662f\u5403\u836f\u7684\u6f5c\u5728\u7ed3\u679c\u4e0e\u4e0d\u5403\u836f\u7684\u6f5c\u5728\u7ed3\u679c\u5728\u6240\u6709\u75c5\u4eba\u4e0a\u7684\u5dee\u503c\u5e73\u5747\u503c\u3002\u4e2a\u4f53\u56e0\u679c\u6548\u5e94<\/span>\uff08Individual Causal Effect, ICE\uff09<\/span>\u5c31\u662f\u5403\u836f\u7684\u6f5c\u5728\u7ed3\u679c\u4e0e\u4e0d\u5403\u836f\u7684\u6f5c\u5728\u7ed3\u679c\u5728\u67d0\u4e2a\u75c5\u4eba\u4e0a\u7684\u5dee\u503c\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u8fd9\u91cc\u8fd8\u6d89\u53ca\u53cd\u4e8b\u5b9e\u7684\u95ee\u9898\uff1a\u5bf9\u4e8e\u67d0\u4e2a\u75c5\u4eba\uff0c\u6211\u4eec\u53ea\u80fd\u89c2\u6d4b\u5230\u4ed6\u5403\u836f\u6216\u4e0d\u5403\u836f\u5176\u4e2d\u4e00\u79cd\u60c5\u51b5\u7684\u7ed3\u679c\uff0c\u60f3\u8981\u63a2\u7a76\u672a\u53d1\u751f\u7684\u53e6\u4e00\u79cd\u60c5\u51b5\u7684\u7ed3\u679c\uff0c\u5c31\u9700\u8981\u5047\u8c61\u5b58\u5728\u4e00\u4e2a\u5e73\u884c\u4e16\u754c\uff0c\u8fd9\u4e2a\u4e16\u754c\u91cc\u75c5\u4eba\u505a\u51fa\u4e86\u4e0e\u4e4b\u524d\u4e0d\u540c\u7684\u9009\u62e9\uff0c\u9664\u6b64\u4e4b\u5916\u90fd\u4fdd\u6301\u5b8c\u5168\u4e00\u81f4\uff0c\u5bf9\u6bd4\u4e24\u4e2a\u4e16\u754c\u7684\u7ed3\u679c\u8fdb\u884c\u6c42\u89e3\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u968f\u673a\u5316\u5b9e\u9a8c<\/span>\uff08Randomized Experiments\uff09<\/span>\u662f\u56e0\u679c\u6548\u5e94\u8bc4\u4f30\u7684\u91d1\u6807\u51c6\u3002\u6bd4\u5982\u5728\u75ab\u82d7\u7814\u53d1\u4e2d\uff0c\u5c31\u9700\u8981\u505a\u53cc\u76f2\u5b9e\u9a8c\u548c\u5355\u76f2\u5b9e\u9a8c\u4ee5\u8bc4\u4f30\u56e0\u679c\u6548\u5e94\u3002\u5728\u8db3\u591f\u5927\u7684\u5b9e\u9a8c\u4eba\u7fa4\u4e2d\uff0c\u901a\u8fc7\u5b8c\u5168\u968f\u673a\u7684\u65b9\u6cd5\u4f7f\u5176\u4e2d\u4e00\u534a\u4eba\u63a5\u79cd\u75ab\u82d7\uff0c\u8fd9\u6837\u5c31\u6392\u9664\u4e86\u5176\u4ed6\u53d8\u91cf\u7684\u5f71\u54cd\uff0c\u6c42\u5f97\u5e73\u5747\u56e0\u679c\u6548\u5e94\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u653f\u7b56\u8bc4\u4f30\u3001\u5065\u5eb7\u533b\u7597\u548c\u5e02\u573a\u8425\u9500\u7b49\u591a\u4e2a\u9886\u57df\u90fd\u6709\u91cd\u8981\u5e94\u7528\uff0c\u4f46\u8fd9\u79cd\u65b9\u6cd5\u7684\u82b1\u9500\u5de8\u5927\uff0c\u4e14\u53ef\u80fd\u6d89\u53ca\u4f26\u7406\u9053\u5fb7\u95ee\u9898\u3002\u90a3\u4e48\u6211\u4eec\u53ef\u5426\u5728\u5de8\u91cf\u7684\u5386\u53f2\u89c2\u6d4b\u6570\u636e\u4e2d\u8fdb\u884c\u6316\u6398\uff0c\u8bc4\u4f30\u51fa\u56e0\u679c\u6548\u5e94\u5462\uff1f<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u6bd4\u5982\u5728\u4e00\u4e2a\u6570\u636e\u96c6\u4e2d\uff0c\u6709\u5403\u836f\u548c\u672a\u5403\u836f\u7684\u4e24\u7fa4\u4eba\u3002\u5982\u679c\u5728\u6570\u636e\u6536\u96c6\u65f6\u662f\u4f7f\u7528\u5355\u76f2\u6216\u53cc\u76f2\u5b9e\u9a8c\uff0c\u90a3\u4e48\u5c31\u53ef\u4ee5\u76f4\u63a5\u53bb\u8ba1\u7b97\u5e73\u5747\u56e0\u679c\u6548\u5e94\u3002\u4f46\u5982\u679c\u6ca1\u6709\u4fdd\u8bc1\u5206\u914d\u836f\u7269\u7684\u968f\u673a\u6027\uff0c\u5c31\u53ef\u80fd\u4f1a\u6709\u4f53\u8d28\u3001\u6027\u522b\u3001\u5e74\u9f84\u7b49\u6df7\u6742\u56e0\u5b50 X \u4f7f\u7ed3\u679c\u4ea7\u751f\u504f\u501a\u3002\u56e0\u679c\u63a8\u7406\u7684\u672c\u8d28\u5c31\u662f\u53bb\u63a7\u5236\u5403\u836f\u548c\u4e0d\u5403\u836f\u7684\u4e24\u7fa4\u4eba\u4e4b\u95f4\u5176\u4ed6\u7279\u5f81\u7684\u5206\u5e03\u3002<\/span><\/section>\n

\n

<\/span><\/section>\n

<\/span><\/section>\n
\n
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<\/p>\n

2. \u56e0\u679c\u6548\u5e94\u8bc4\u4f30\u7684\u65b9\u6cd5<\/strong><\/span><\/p>\n

<\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

 <\/span>     <\/span>       <\/span><\/p>\n<\/h1>\n

\u73b0\u5728\u6211\u4eec\u8003\u8651\u5e72\u9884\u53d8\u91cf\u4e3a\u4e8c\u503c\u7684\u60c5\u51b5\uff0c\u8981\u53bb\u5e73\u8861\u5176\u4ed6\u53d8\u91cf\u7684\u5206\u5e03\uff0c\u518d\u505a\u56e0\u679c\u6548\u5e94\u8bc4\u4f30\u3002\u5728\u8fd9\u91cc\u4ecb\u7ecd\u4e09\u79cd\u65b9\u6cd5\uff1aMatching\u3001Propensity Score Based Methods \u548c Directly Confounder Balancing\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u9996\u5148\u7ed9\u51fa\u5728\u89c2\u6d4b\u6570\u636e\u4e0b\u505a\u56e0\u679c\u63a8\u7406\u7684\u4e09\u4e2a\u5047\u8bbe\uff1a<\/span><\/section>\n

<\/span><\/section>\n
\u4e00\u3001Stable Unit Treatment Value<\/span> (SUTV) <\/span>\u53d8\u91cf\u4e4b\u95f4\u662f\u72ec\u7acb\u7684\uff1a\u6211\u662f\u5426\u5403\u836f\u7684\u7ed3\u679c\u4e0d\u53d7\u522b\u4eba\u662f\u5426\u5403\u836f\u7684\u5f71\u54cd\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u4e8c\u3001Unconfounderness \u6240\u6709\u6df7\u6dc6\u53d8\u91cf\u90fd\u88ab\u89c2\u6d4b\u5230\u4e86\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u4e09\u3001Overlap \u5404\u79cd\u5e72\u9884\u53d8\u91cf\u7684\u53d6\u503c\u6982\u7387\u90fd\u5e94\u8be5\u57280\u52301\u4e4b\u95f4\u3002<\/span><\/section>\n

\n

<\/section>\n
2.1 Matching<\/span><\/strong><\/span><\/section>\n<\/h2>\n
 <\/span>      <\/span>       <\/span><\/section>\n
\u7b2c\u4e00\u79cd\u65b9\u6cd5\u53eb Mathching\u3002\u5728\u4e24\u4e2a\u4e0d\u540c\u7684\u5904\u7406\u7fa4\u4f53\u4e4b\u95f4\uff0c\u9010\u4e00\u5bfb\u627e\u5e76\u5339\u914d\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u90fd\u76f8\u540c\u7684\u4e2a\u4f53\uff0c\u5982\u679c\u627e\u4e0d\u5230\u5c31\u820d\u5f03\u6b64\u4e2a\u4f53\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u4fdd\u8bc1\u6709\u5339\u914d\u7684\u90a3\u4e9b\u6837\u672c\u4e2d\u5176\u4ed6\u7279\u5f81\u7684\u5206\u5e03\u5728\u4e00\u5b9a\u8303\u56f4\u5185\u662f\u76f8\u4f3c\u7684\uff0c\u8fdb\u800c\u521d\u6b65\u53bb\u8bc4\u4f30\u56e0\u679c\u6548\u5e94\u3002<\/span><\/section>\n

<\/span><\/section>\n
Matching \u7684\u95ee\u9898\u5c31\u662f\u5982\u4f55\u53bb\u8bc4\u4f30\u4e24\u4e2a\u4e2a\u4f53\u7684\u76f8\u4f3c\u5ea6\uff0c\u5e76\u9700\u8981\u8bbe\u5b9a\u53ef\u4ee5\u63a5\u53d7\u7684\u5dee\u5f02\u9608\u503c\uff0c\u5728\u820d\u5f03\u6837\u672c\u6570\u548c\u76f8\u4f3c\u5ea6\u4e4b\u95f4\u8fdb\u884c\u5e73\u8861\u3002\u5176\u6b21\u5bf9\u4e8e\u9ad8\u7ef4\u6570\u636e\uff0c\u5f88\u96be\u627e\u5230\u76f8\u4f3c\u6837\u672c\uff1a\u6bd4\u5982\u670910\u4e2a\u4e8c\u503c\u53d8\u91cf\uff0c\u5c31\u9700\u8981\u81f3\u5c111025\u4e2a\u6837\u672c\u624d\u80fd\u4fdd\u8bc1\u627e\u5230\u76f8\u540c\u4e2a\u4f53\u3002<\/span><\/section>\n

<\/span><\/section>\n

\n
2.2 Propensity Score Based Methods<\/span><\/strong><\/span><\/section>\n<\/h2>\n
       <\/span>      <\/span><\/section>\n
\u7b2c\u4e8c\u7c7b\u65b9\u6cd5\u662f\u57fa\u4e8e\u503e\u5411\u6307\u6570\/\u503e\u5411\u5f97\u5206<\/span>\uff08Propensity Score\uff09<\/span>\u7684\u3002\u503e\u5411\u6307\u6570\u7684\u5b9a\u4e49\u5c31\u662f\u5728\u5e72\u9884\u53d8\u91cf\u4e4b\u5916\u7684\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u4e3a\u4e00\u5b9a\u503c\u7684\u6761\u4ef6\u4e0b\uff0c\u4e2a\u4f53\u88ab\u5904\u7406\u7684\u6982\u7387\u3002Rubin \u8bc1\u660e\u4e86\u5728\u7ed9\u5b9a\u503e\u5411\u6307\u6570\u7684\u60c5\u51b5\u4e0b\uff0cUnconfounderness \u5047\u8bbe\u5c31\u53ef\u4ee5\u6ee1\u8db3\u3002\u503e\u5411\u6307\u6570\u5176\u5b9e\u6982\u62ec\u4e86\u7fa4\u4f53\u7684\u7279\u5f81\u53d8\u91cf\uff0c\u5982\u679c\u4e24\u4e2a\u7fa4\u4f53\u7684\u503e\u5411\u6307\u6570\u76f8\u540c\uff0c\u90a3\u4ed6\u4eec\u7684\u5e72\u9884\u53d8\u91cf\u5c31\u662f\u4e0e\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u76f8\u72ec\u7acb\u7684\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u6bd4\u5982\u8bf4\uff0c\u5982\u679c\u80fd\u4fdd\u8bc1\u4e24\u7fa4\u4eba\u7684\u4ed6\u5403\u836f\u7684\u6982\u7387\u5b8c\u5168\u4e00\u6837\uff0c\u90a3\u4e48\u53ef\u4ee5\u8bf4\u8fd9\u4e24\u7fa4\u4eba\u5176\u4ed6\u7279\u5f81\u5206\u5e03\u4e5f\u662f\u4e00\u6837\u3002<\/span> <\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u503e\u5411\u6307\u6570\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u662f\u89c2\u6d4b\u4e0d\u5230\u7684\uff0c\u4f46\u53ef\u4ee5\u4f7f\u7528\u6709\u76d1\u7763\u5b66\u4e60\u7684\u65b9\u6cd5\u8fdb\u884c\u4f30\u8ba1\u3002\u6839\u636e\u4f30\u8ba1\u5230\u7684\u503e\u5411\u6307\u6570\uff0c\u7b2c\u4e00\u79cd\u65b9\u6cd5\u5c31\u662f\u53bb\u505a Matching\uff0c\u8fd9\u6837\u80fd\u89e3\u51b3\u5728\u9ad8\u7ef4\u6570\u636e\u4e2d\u96be\u4ee5\u627e\u5230\u76f8\u4f3c\u6837\u672c\u7684\u95ee\u9898\u3002<\/span><\/section>\n
       <\/span>       <\/span>       <\/span>              <\/span>       <\/span><\/section>\n
\u7b2c\u4e8c\u79cd\u662f\u4f7f\u7528 Inverse of Propensity Weighting \u65b9\u6cd5\uff0c\u5bf9\u4e8e\u5e72\u9884\u53d8\u91cf\u4e3a1\u7684\u6837\u672c\u4f7f\u7528\u503e\u5411\u6307\u6570\u7684\u5012\u6570\u8fdb\u884c\u52a0\u6743\uff0c\u800c\u5bf9\u4e8e\u4e3a0\u7684\u6837\u672c\u4f7f\u7528<\/span>\uff081-\u503e\u5411\u6307\u6570\uff09<\/span>\u7684\u5012\u6570\u8fdb\u884c\u52a0\u6743\uff0c\u4e24\u7c7b\u6837\u672c\u7684\u52a0\u6743\u5e73\u5747\u503c\u4e4b\u5dee\u5c31\u662f\u5e73\u5747\u56e0\u679c\u6548\u5e94\u7684\u5927\u5c0f\u3002\u8fd9\u91cc\u6709\u4e00\u4e2a\u5047\u8bbe\uff0c\u5c31\u662f\u4f30\u8ba1\u51fa\u7684\u503e\u5411\u6307\u6570\u4e0e\u771f\u5b9e\u7684\u503e\u5411\u6307\u6570\u662f\u76f8\u7b49\u7684\u3002<\/span><\/section>\n

<\/span><\/section>\n
\u56e0\u6b64\u8fd9\u4e2a\u65b9\u6cd5\u6709\u4e24\u4e2a\u5f31\u70b9\uff0c\u4e00\u662f\u9700\u8981\u5bf9\u503e\u5411\u6307\u6570\u7684\u4f30\u8ba1\u8db3\u591f\u7cbe\u786e\uff1b\u4e8c\u662f\u5982\u679c\u503e\u5411\u6307\u6570\u8fc7\u4e8e\u8d8b\u8fd10\u62161\uff0c\u5c31\u4f1a\u5bfc\u81f4\u67d0\u4e9b\u6743\u91cd\u7684\u503c\u8fc7\u9ad8\uff0c\u4f7f\u4f30\u8ba1\u51fa\u7684\u5e73\u5747\u56e0\u679c\u6548\u5e94\u7684\u65b9\u5dee\u8fc7\u5927\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u7b2c\u4e09\u79cd\u65b9\u6cd5\u53eb Doubly Robust\u3002\u8fd9\u4e2a\u65b9\u6cd5\u9700\u8981\u6839\u636e\u5df2\u6709\u6570\u636e\uff0c\u518d\u5b66\u4e60\u4e00\u4e2a\u9884\u6d4b\u7684\u6a21\u578b\uff0c\u53cd\u4e8b\u5b9e\u8bc4\u4f30\u67d0\u4e2a\u4e2a\u4f53\u5728\u5e72\u9884\u53d8\u91cf\u53d8\u5316\u540e\uff0c\u7ed3\u679c\u53d8\u91cf\u7684\u671f\u671b\u503c\u3002\u53ea\u8981\u503e\u5411\u6307\u6570\u7684\u4f30\u8ba1\u6a21\u578b\u548c\u53cd\u4e8b\u5b9e\u9884\u6d4b\u6a21\u578b\u4e2d\u6709\u4e00\u4e2a\u662f\u5bf9\u7684\uff0c\u8ba1\u7b97\u51fa\u7684\u5e73\u5747\u56e0\u679c\u6548\u5e94\u5c31\u662f\u65e0\u504f\u7684\uff1b\u4f46\u5982\u679c\u4e24\u4e2a\u6a21\u578b\u4f30\u8ba1\u90fd\u662f\u9519\u8bef\u7684\uff0c\u90a3\u4ea7\u751f\u7684\u8bef\u5dee\u53ef\u80fd\u4f1a\u975e\u5e38\u5927\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u4ee5\u4e0a\u7684\u8fd9\u4e09\u79cd\u57fa\u4e8e\u503e\u5411\u6307\u6570\u7684\u65b9\u6cd5\u6bd4\u8f83\u7c97\u66b4\uff0c\u628a\u5e72\u9884\u53d8\u91cf\u548c\u7ed3\u679c\u53d8\u91cf\u4e4b\u5916\u7684\u6240\u6709\u53d8\u91cf\u90fd\u5f53\u4f5c\u6df7\u6dc6\u53d8\u91cf\u3002\u800c\u5728\u9ad8\u7ef4\u6570\u636e\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u7cbe\u51c6\u5730\u627e\u51fa\u90a3\u4e9b\u771f\u6b63\u9700\u8981\u63a7\u5236\u7684\u6df7\u6dc6\u53d8\u91cf\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u6570\u636e\u9a71\u52a8\u7684\u53d8\u91cf\u5206\u89e3\u7b97\u6cd5<\/span>\uff08D\u00b2VD\uff09<\/span>\uff0c\u5c06\u5e72\u9884\u53d8\u91cf\u548c\u7ed3\u679c\u53d8\u91cf\u4e4b\u5916\u7684\u5176\u4ed6\u53d8\u91cf\u5206\u4e3a\u4e86\u4e09\u7c7b\uff1a<\/span><\/section>\n

<\/span><\/section>\n

1. \u6df7\u6dc6\u53d8\u91cf<\/span>\uff08Confounders\uff09<\/span>\uff1a\u65e2\u4f1a\u5f71\u54cd\u5230\u5e72\u9884\u53d8\u91cf\uff0c\u8fd8\u4f1a\u5f71\u54cd\u5230\u7ed3\u679c\u53d8\u91cf<\/span><\/p>\n

2. \u8c03\u6574\u53d8\u91cf<\/span>\uff08Adjustment Variables\uff09<\/span>\uff1a\u4e0e\u5e72\u9884\u53d8\u91cf\u72ec\u7acb\uff0c\u4f46\u4f1a\u5f71\u54cd\u5230\u7ed3\u679c\u53d8\u91cf<\/span><\/p>\n

3. \u65e0\u5173\u53d8\u91cf\uff1a\u4e0d\u4f1a\u76f4\u63a5\u5f71\u54cd\u5230\u5e72\u9884\u53d8\u91cf\u4e0e\u7ed3\u679c\u53d8\u91cf<\/span><\/p>\n


<\/span><\/section>\n
\u8fdb\u884c\u5206\u7c7b\u4e4b\u540e\uff0c\u5c31\u53ef\u4ee5\u53ea\u7528\u6df7\u6dc6\u53d8\u91cf\u96c6\u53bb\u4f30\u8ba1\u503e\u5411\u6307\u6570\u3002\u800c\u8c03\u6574\u53d8\u91cf\u96c6\u4f1a\u88ab\u89c6\u4e3a\u5bf9\u7ed3\u679c\u53d8\u91cf\u7684\u566a\u58f0\uff0c\u8fdb\u884c\u6d88\u51cf\u3002\u6700\u540e\u4f7f\u7528\u7ecf\u8fc7\u8c03\u6574\u7684\u7ed3\u679c\uff0c\u53bb\u4f30\u8ba1\u5e73\u5747\u56e0\u679c\u6548\u5e94\u3002\u6211\u4eec\u4ece\u7406\u8bba\u4e0a\u8bc1\u660e\u4e86\uff0c\u4f7f\u7528\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5f97\u5230\u65e0\u504f\u7684\u5e73\u5747\u56e0\u679c\u6548\u5e94\u4f30\u8ba1\uff0c\u800c\u4e14\u4f30\u8ba1\u7ed3\u679c\u7684\u65b9\u5dee\u4e0d\u4f1a\u5927\u4e8e Inverse of Propensity Weighting \u65b9\u6cd5\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u6211\u7684\u5b66\u751f\u53c8\u62d3\u5c55\u4e86\u6211\u7684\u5de5\u4f5c\uff0c\u4e0e\u8868\u5f81\u5b66\u4e60\u76f8\u7ed3\u5408\uff0c\u63d0\u51fa\u4e86 Decomposed Representation\u3002\u5176\u4e2d\u589e\u52a0\u4e86\u4e00\u7c7b\u53d8\u91cf\uff1a<\/span><\/section>\n

<\/span><\/section>\n
4. \u5de5\u5177\u53d8\u91cf\uff1a\u4e0e\u7ed3\u679c\u53d8\u91cf\u72ec\u7acb\uff0c\u4f46\u4f1a\u5f71\u54cd\u5230\u5e72\u9884\u53d8\u91cf<\/span><\/section>\n

<\/span><\/section>\n
\u901a\u8fc7\u5b9e\u9a8c\u53d1\u73b0\uff0c\u4e0e\u4f20\u7edf\u65b9\u6cd5\u76f8\u6bd4\uff0c\u6b64\u65b9\u6cd5\u53ef\u4ee5\u5f88\u51c6\u786e\u5730\u628a\u53d8\u91cf\u5206\u79bb\u51fa\u6765\uff0c\u63d0\u9ad8\u5e73\u5747\u56e0\u679c\u6548\u5e94\u7684\u51c6\u786e\u6027\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n

\n
2.3 Directly Confounder Balancing<\/span><\/strong><\/span><\/section>\n

<\/span><\/section>\n<\/h2>\n
\u603b\u7684\u6765\u8bf4\uff0c\u57fa\u4e8e\u503e\u5411\u6307\u6570\u7684\u65b9\u6cd5\u8fd8\u662f\u9700\u8981\u4f30\u8ba1\u503e\u5411\u6307\u6570\u7684\u6a21\u578b\u662f\u51c6\u786e\u7684\u3002\u65e2\u7136\u503e\u5411\u6307\u6570\u5c31\u662f\u7528\u4e8e\u8ba1\u7b97\u6743\u91cd\u7684\uff0c\u6211\u4eec\u53ef\u4e0d\u53ef\u4ee5\u76f4\u63a5\u53bb\u4f30\u8ba1\u6743\u91cd\u5462\uff1f<\/span><\/section>\n

<\/span><\/section>\n
\u7b2c\u4e09\u7c7b\u65b9\u6cd5\u5c31\u662f Directly Confounder Balancing\uff0c\u76f4\u63a5\u5bf9\u6837\u672c\u6743\u91cd\u8fdb\u884c\u5b66\u4e60\u3002\u8fd9\u7c7b\u65b9\u6cd5\u7684\u52a8\u673a\u5c31\u662f\u53bb\u63a7\u5236\u5728\u5e72\u9884\u53d8\u91cf\u4e0b\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u7684\u5206\u5e03\u3002\u800c\u4e00\u4e2a\u53d8\u91cf\u7684\u6240\u6709\u9636\u7684\u77e9<\/span>\uff08moment\uff09<\/span>\u53ef\u4ee5\u552f\u4e00\u786e\u5b9a\u5b83\u7684\u5206\u5e03\uff0c\u6240\u4ee5\u53ea\u9700\u8981\u53bb\u63a7\u5236\u5b83\u6240\u6709\u9636\u7684\u77e9<\/span>\uff08\u6bd4\u5982\u4e00\u9636\u77e9\u5c31\u662f\u5747\u503c\uff0c\u4e8c\u9636\u77e9\u5c31\u662f\u65b9\u5dee\uff09<\/span>\u5c31\u53ef\u4e86\u3002\u5728\u5b9e\u9a8c\u4e2d\u6211\u4eec\u53d1\u73b0\uff0c\u53ea\u8003\u8651\u4e00\u9636\u77e9\u5c31\u53ef\u4ee5\u8fbe\u5230\u5f88\u597d\u7684\u6548\u679c\uff0c\u56e0\u6b64\u8fd9\u91cc\u5148\u4e0d\u8003\u8651\u4e8c\u9636\u53ca\u4ee5\u4e0a\u7684\u77e9\u3002\u901a\u8fc7\u8fd9\u4e2a\u624b\u6bb5\u5c31\u53ef\u4ee5\u76f4\u63a5\u5b66\u4e60\u6837\u672c\u6743\u91cd\uff0c\u8fdb\u884c\u5e73\u5747\u56e0\u679c\u6548\u5e94\u4f30\u8ba1\u4e86\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u8fd9\u4e2a\u6982\u5ff5\u9996\u5148\u51fa\u73b0\u4e8e Entrophy Balancing \u65b9\u6cd5\u4e4b\u4e2d\uff0c\u901a\u8fc7\u5b66\u4e60\u6837\u672c\u6743\u91cd\uff0c\u4f7f\u7279\u5f81\u53d8\u91cf\u7684\u5206\u5e03\u5728\u4e00\u9636\u77e9\u4e0a\u4e00\u81f4\uff0c\u540c\u65f6\u8fd8\u7ea6\u675f\u4e86\u6743\u91cd\u7684\u71b5<\/span>\uff08Entropy\uff09<\/span>\u3002\u4f46\u8fd9\u4e2a\u65b9\u6cd5\u7684\u95ee\u9898\u4e5f\u662f\u5c06\u6240\u6709\u53d8\u91cf\u90fd\u540c\u7b49\u5bf9\u5f85\u4e86\uff0c\u628a\u8fc7\u591a\u53d8\u91cf\u8003\u8651\u4e3a\u6df7\u6742\u53d8\u91cf\u3002<\/span><\/section>\n
       <\/span>      <\/span><\/section>\n
\u7b2c\u4e8c\u79cd\u65b9\u6cd5\u53eb Approximate Residual Balancing\u3002\u7b2c\u4e00\u6b65\u4e5f\u662f\u901a\u8fc7\u8ba1\u7b97\u6837\u672c\u6743\u91cd\u4f7f\u5f97\u4e00\u9636\u77e9\u4e00\u81f4\uff0c\u7b2c\u4e8c\u6b65\u4e0e Doubly Robust \u7684\u601d\u60f3\u4e00\u81f4\uff0c\u52a0\u5165\u4e86\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u5728\u7b2c\u4e09\u6b65\u7ed3\u5408\u4e86\u524d\u4e24\u6b65\u7684\u7ed3\u679c\u4f30\u8ba1\u5e73\u5747\u56e0\u679c\u6548\u5e94\u3002\u53ea\u8981\u6837\u672c\u6743\u91cd\u7684\u4f30\u8ba1\u548c\u53cd\u4e8b\u5b9e\u9884\u6d4b\u6a21\u578b\u4e2d\u6709\u4e00\u4e2a\u662f\u5bf9\u7684\uff0c\u8ba1\u7b97\u51fa\u7684\u5e73\u5747\u56e0\u679c\u6548\u5e94\u5c31\u662f\u65e0\u504f\u7684\u3002\u4f46\u8fd9\u91cc\u4e5f\u662f\u5c06\u6240\u6709\u53d8\u91cf\u90fd\u540c\u7b49\u5bf9\u5f85\u4e86\u3002<\/span><\/section>\n
       <\/span>       <\/span>       <\/span>       <\/span><\/section>\n
\u6211\u4eec\u63d0\u51fa\u7684\u65b9\u6cd5\u53eb\u505a\u6df7\u6dc6\u53d8\u91cf\u533a\u5206\u6027\u5e73\u8861<\/span>\uff08Differentiated Confounder Balancing, DCB\uff09<\/span>\uff0c\u8003\u8651\u5230\u7684\u5c31\u662f\u4e0d\u540c\u7684\u7279\u5f81\u53d8\u91cf\u5bf9\u4e8e\u5e73\u5747\u56e0\u679c\u6548\u5e94\u7684\u5f71\u54cd\u662f\u4e0d\u540c\u7684\u3002\u6211\u4eec\u5728\u4f20\u7edf\u65b9\u6cd5\u4e0a\u52a0\u5165\u4e86\u6df7\u6dc6\u53d8\u91cf\u6743\u91cd<\/span>\uff08Confounder Weights\uff09<\/span>\u03b2\uff1a\u5f53\u03b2\u4e3a0\u65f6\uff0c\u4ee3\u8868\u6240\u5bf9\u5e94\u7684\u53d8\u91cf\u4e0d\u662f\u6df7\u6dc6\u53d8\u91cf\uff0c\u5bf9\u56e0\u679c\u6548\u5e94\u4e0d\u4f1a\u5e26\u6765\u5f71\u54cd\uff1b\u5f53\u03b2\u8f83\u5927\u65f6\uff0c\u8bf4\u660e\u6b64\u53d8\u91cf\u5bf9\u56e0\u679c\u6548\u5e94\u7684\u5f71\u54cd\u8f83\u5927\u3002\u5176\u4e2d\u7684\u03b2\u6b63\u597d\u662f\u4ece\u5e72\u9884\u53d8\u91cf\u7684\u589e\u5e7f\u5230\u7ed3\u679c\u53d8\u91cf\u7684\u56de\u5f52\u7cfb\u6570\u3002\u901a\u8fc7\u4e00\u7cfb\u5217\u5b9e\u9a8c\u53d1\u73b0\uff0c\u6211\u4eec\u7684\u65b9\u6cd5\u5728\u9ad8\u7ef4\u6570\u636e\u4e0b\u5bf9\u5e73\u5747\u56e0\u679c\u6548\u5e94\u7684\u4f30\u8ba1\u504f\u5dee\u51e0\u4e4e\u4e3a0\uff0c\u4f18\u4e8e\u5176\u4ed6\u65b9\u6cd5\u3002<\/span><\/section>\n
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2.4 Generative Adversial De-confounding<\/span><\/strong><\/span><\/section>\n

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\u4e0a\u8ff0\u7684\u6240\u6709\u65b9\u6cd5\u4e2d\uff0c\u5e72\u9884\u53d8\u91cf\u90fd\u662f\u4e8c\u503c\u7684\uff0c\u90a3\u5982\u4f55\u53bb\u5904\u7406\u591a\u503c\u7684\u6216\u8005\u8fde\u7eed\u7684\u5e72\u9884\u53d8\u91cf\u5462\uff1f\u6211\u4eec\u4eca\u5e74\u7684\u4e00\u4e2a\u5de5\u4f5c Generative Adversial De-confounding \u5c31\u5c1d\u8bd5\u4f30\u8ba1\u8fd9\u7c7b\u590d\u6742\u60c5\u51b5\u4e0b\u5e72\u9884\u53d8\u91cf\u4e0e\u7ed3\u679c\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u6548\u5e94\u3002\u8fd9\u91cc\u7684\u6838\u5fc3\u601d\u60f3\u5c31\u662f\u5982\u4f55\u4fdd\u8bc1\u5e72\u9884\u53d8\u91cf\u4e0e\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u7684\u5206\u5e03\u76f8\u72ec\u7acb\u3002\u6211\u4eec\u5229\u7528\u4e86 GAN \u7684\u601d\u60f3\uff0c\u53bb\u51ed\u7a7a\u6784\u9020\u51fa\u53e6\u4e00\u4e2a\u5e72\u9884\u53d8\u91cf\u4e0e\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u76f8\u72ec\u7acb\u7684\u5206\u5e03\u3002<\/span><\/section>\n
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\u6211\u4eec\u4f7f\u7528\u4e86\u968f\u673a\u6253\u4e71<\/span>\uff08Random Shuffle\uff09<\/span>\u7684\u65b9\u6cd5\uff0c\u53ea\u6253\u4e71\u5e72\u9884\u53d8\u91cf\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u4f7f\u5e72\u9884\u53d8\u91cf\u4e0e\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u76f8\u72ec\uff0c\u5e76\u4fdd\u7559\u4e86\u4e24\u8005\u7684\u5206\u5e03\u3002\u6211\u4eec\u518d\u4f7f\u7528\u6837\u672c\u6743\u91cd\u4f30\u8ba1\u7684\u65b9\u6cd5\uff0c\u4f7f\u539f\u6765\u6570\u636e\u96c6\u7684\u52a0\u6743\u5206\u5e03\u7ed3\u679c\u4e0e\u6784\u9020\u51fa\u7684\u5206\u5e03\u76f8\u4e00\u81f4\u3002\u5728\u5b9e\u9a8c\u4e2d\uff0c\u53ef\u4ee5\u53d1\u73b0\u6211\u4eec\u7684\u65b9\u6cd5\u6210\u529f\u964d\u4f4e\u4e86\u5e72\u9884\u53d8\u91cf\u4e0e\u5176\u4ed6\u7279\u5f81\u53d8\u91cf\u7684\u76f8\u5173\u6027\uff0c\u5e76\u6709\u6548\u63d0\u9ad8\u4e86\u56e0\u679c\u6548\u5e94\u8bc4\u4f30\u7684\u51c6\u786e\u6027\u3002<\/span><\/section>\n
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\u53c2\u8003\u6587\u732e\uff1a<\/span><\/strong><\/p>\n<\/h1>\n

[1] Kuang, K., Cui, P., Zou, H., Li, B., Tao, J., Wu, F., & Yang, S. (2020). Data-Driven Variable Decomposition for Treatment Effect Estimation. IEEE Transactions on Knowledge and Data Engineering.<\/span><\/section>\n
[2] Wu, A., Kuang, K., Yuan, J., Li, B., Zhou, P., Tao, J., … & Wu, F. (2020). Learning Decomposed Representation for Counterfactual Inference. arXiv preprint arXiv:2006.07040.<\/span><\/section>\n
[3] Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., … & Jiang, Z. (2020). Causal Inference. Engineering, 6(3), 253-263.<\/span><\/section>\n
[4] Kuang, K., Cui, P., Li, B., Jiang, M., Wang, Y., Wu, F., & Yang, S. (2019). Treatment Effect Estimation via Differentiated Confounder Balancing and Regression. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 1-25.<\/span><\/section>\n
[5] Kuang, K., Cui, P., Li, B., Jiang, M., & Yang, S. (2017, August). Estimating treatment effect in the wild via differentiated confounder balancing. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 265-274).<\/span><\/section>\n
[6] Kuang, K., Cui, P., Li, B., Jiang, M., Yang, S., & Wang, F. (2017, February). Treatment effect estimation with data-driven variable decomposition. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.<\/span><\/section>\n

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\u7cfb\u5217\u8bfb\u4e66\u4f1a\u62a5\u540d<\/strong><\/span><\/p>\n

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\u5927\u6570\u636e\u65f6\u4ee3\u7684\u4e0b\u4e00\u573a\u53d8\u9769\u2014\u2014\u56e0\u679c\u9769\u547d\u6b63\u5728\u915d\u917f\u4e4b\u4e2d\uff0c\u901a\u8fc7\u878d\u5408\u56e0\u679c\u63a8\u7406\u548c\u673a\u5668\u5b66\u4e60\u800c\u6784\u5efa\u51fa\u6765\u7684Causal AI\u7cfb\u7edf\uff0c\u6709\u671b\u5960\u5b9a\u5f3a\u4eba\u5de5\u667a\u80fd\u7684\u57fa\u77f3\u3002<\/span><\/p>\n


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\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u5317\u4eac\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\uff0c\u9080\u8bf7\u4e86\u4e00\u6279\u5bf9\u56e0\u679c\u79d1\u5b66\u4e0eCasual AI\u611f\u5174\u8da3\u7684\u7814\u7a76\u8005\uff0c\u5f00\u5c55\u4e3a\u671f2-3\u4e2a\u6708\u7684\u7cfb\u5217\u7ebf\u4e0a\u8bfb\u4e66\u4f1a\uff0c\u7814\u8bfb\u7ecf\u5178\u548c\u524d\u6cbf\u8bba\u6587\uff0c\u5e76\u5c1d\u8bd5\u96c6\u4f53\u64b0\u5199\u4e00\u90e8\u4e66\u7c4d\u3002\u5982\u679c\u4f60\u4e5f\u4ece\u4e8b\u76f8\u5173\u7684\u7814\u7a76\u3001\u5e94\u7528\u5de5\u4f5c\uff0c\u6b22\u8fce\u62a5\u540d\uff0c\u53c2\u4e0e\u8bfb\u4e66\u4f1a\u7684\u8ba8\u8bba\uff01<\/span><\/p>\n

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\u65f6\u95f4\uff1a<\/span><\/strong>9\u670820\u65e5\u8d77\uff0c\u6bcf\u5468\u65e5\u665a19:00-21:00\uff0c\u6301\u7eed\u7ea62-3\u4e2a\u6708<\/span><\/section>\n
\u6a21\u5f0f\uff1a<\/span><\/strong>\u7ebf\u4e0a\u95ed\u95e8\u8bfb\u4e66\u4f1a\uff1b\u6536\u8d39-\u9000\u6b3e\u7684\u4fdd\u8bc1\u91d1\u6a21\u5f0f\uff1b\u8bfb\u4e66\u4f1a\u6210\u5458\u8ba4\u9886\u89e3\u8bfb\u8bba\u6587<\/span><\/section>\n
\u8d39\u7528\uff1a<\/span><\/strong>299\/\u4eba<\/span><\/section>\n

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\u56e0\u679c\u79d1\u5b66\u4e0e Causal AI \u7cfb\u5217\u8bfb\u4e66\u4f1a | \u4f17\u5305\u51fa\u4e66<\/a><\/section>\n


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\u9488\u5bf9\u8bfb\u4e66\u4f1a\u7684\u4e3b\u9898\uff0c\u7531\u53d1\u8d77\u4eba\u9f9a\u9e64\u626c\u8bbe\u7f6e\u597d\u4e86\u5185\u5bb9\u6846\u67b6\uff0c\u6bcf\u4e2a\u4e3b\u9898\u4e0b\u6709\u4e00\u4e2a\u8d1f\u8d23\u4eba\u6765\u8d1f\u8d23\u7ef4\u62a4\u7ec4\u7ec7\u76f8\u5173\u5185\u5bb9\uff0c\u76ee\u524d\u5df2\u7ecf\u5b9a\u597d\u7684\u5982\u56fe\u6240\u793a\uff0c\u6b22\u8fce\u5bf9\u4e3b\u9898\u611f\u5174\u8da3\u7684\u8054\u7cfb\u76f8\u5173\u8d1f\u8d23\u4eba\uff0c\u4ee5\u53ca\u6765\u8ba4\u9886\u76f8\u5173\u4e3b\u9898~<\/span><\/p>\n


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\u96c6\u667a\u6591\u56fe\u6536\u5f55\u6765\u81ea Nature\u3001Science <\/em>\u7b49\u9876\u520a\u53caarXiv\u9884\u5370\u672c\u7f51\u7ad9\u7684\u6700\u65b0\u8bba\u6587\uff0c\u5305\u62ec\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u7b49\u7814\u7a76\u65b9\u5411\u3002\u6bcf\u5929\u6301\u7eed\u66f4\u65b0\uff0c\u626b\u7801\u5373\u53ef\u83b7\u53d6\uff1a<\/span><\/p>\n


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Science\u7ecf\u5178\u8bba\u6587\uff1a\u5982\u4f55\u68c0\u6d4b\u590d\u6742\u751f\u6001\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u5173\u7cfb\uff1f<\/strong><\/span><\/a>
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\u6d1e\u5bdf\u8c1c\u9898 : \u7528\u7b80\u5355\u7684\u6570\u5b66\u6a21\u578b\u63a2\u7d22\u590d\u6742\u7684\u56e0\u679c\u5173\u7cfb<\/strong><\/span><\/a><\/section>\n
\u501f\u52a9\u56e0\u679c\u63a8\u65ad\uff0c\u66f4\u9c81\u68d2\u7684\u673a\u5668\u5b66\u4e60\u6765\u4e86\uff01<\/a><\/strong><\/span>
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\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/a><\/span><\/strong><\/span><\/section>\n

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\u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org
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\u52a0\u5165\u201c\u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240\u201d<\/span><\/strong><\/span><\/p>\n

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