{"id":23226,"date":"2021-01-11T21:09:43","date_gmt":"2021-01-11T13:09:43","guid":{"rendered":"https:\/\/swarma.org\/?p=23226"},"modified":"2021-01-11T21:09:43","modified_gmt":"2021-01-11T13:09:43","slug":"%e5%9b%a0%e6%9e%9c%e6%8e%a8%e6%96%ad%e9%a2%86%e5%9f%9f%e6%96%b0%e4%b9%a6%ef%bc%88%e9%99%84pdf%ef%bc%89%ef%bc%9acausal-inference-what-if","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=23226","title":{"rendered":"\u56e0\u679c\u63a8\u65ad\u9886\u57df\u65b0\u4e66\uff08\u9644PDF\uff09\uff1aCausal Inference: What If"},"content":{"rendered":"
\u96c6\u667a\u6591\u56fe\u9876\u520a\u8bba\u6587\u901f\u9012\u680f\u76ee\u4e0a\u7ebf\u4ee5\u6765\uff0c\u6301\u7eed\u6536\u5f55\u6765\u81eaNature\u3001Science\u7b49\u9876\u520a\u7684\u6700\u65b0\u8bba\u6587\uff0c\u8ffd\u8e2a\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u7b49\u9886\u57df\u7684\u524d\u6cbf\u8fdb\u5c55\u3002\u73b0\u5728\u6b63\u5f0f\u63a8\u51fa\u8ba2\u9605\u529f\u80fd\uff0c\u6bcf\u5468\u901a\u8fc7\u5fae\u4fe1\u670d\u52a1\u53f7\u300c\u96c6\u667a\u6591\u56fe\u300d\u63a8\u9001\u8bba\u6587\u4fe1\u606f\u3002\u626b\u63cf\u4e0a\u65b9\u4e8c\u7ef4\u7801\u5373\u53ef\u4e00\u952e\u8ba2\u9605\u3002<\/p>\n

\u56e0\u679c\u63a8\u65ad\u662f\u5f53\u4eca\u673a\u5668\u5b66\u4e60\u9886\u57df\u7099\u624b\u53ef\u70ed\u7684\u7814\u7a76\u65b9\u5411\uff0c\u800c\u56fe\u7075\u5956\u5f97\u4e3b\u8d1d\u53f6\u65af\u7f51\u7edc\u4e4b\u7236 Judea Pearl \u7684\u8457\u4f5c\u300a\u4e3a\u4ec0\u4e48\u300b\u66f4\u662f\u8ba9\u8be5\u9886\u57df\u7684\u77e5\u8bc6\u5f97\u4ee5\u666e\u53ca\u3002\u800c\u57282020\u5e74\u672b\uff0c\u7531\u54c8\u4f5b\u5927\u5b66\u516c\u5171\u536b\u751f\u5b66\u9662\u7684 Miguel Hernan \u4e0e Jamie Robins \u6559\u6388\u5408\u4f5c\u5b8c\u6210\u7684\u56e0\u679c\u63a8\u65ad\u9886\u57df\u7684\u65b0\u4e66 Causal Inference: What If\uff0c\u66f4\u662f\u8be6\u7ec6\u5168\u9762\u5730\u9610\u8ff0\u4e86\u56e0\u679c\u63a8\u65ad\u9886\u57df\u7684\u57fa\u672c\u6982\u5ff5\u4e0e\u80cc\u540e\u7684\u7406\u8bba\u57fa\u7840\u3002
 
Causal Inference: What If \u4e66\u7c4d\u540d\u79f0\uff1aCausal Inference: What If\u626b\u7801\u4e0b\u8f7d\uff08\u53ca\u9605\u8bfb\u539f\u6587\uff09
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\u4f9d\u7167\u4e8c\u4f4d\u4f5c\u8005\u7684\u7f16\u6392\uff0c\u672c\u4e66\u7684\u96be\u5ea6\u662f\u9010\u7ea7\u9012\u589e\u7684\u3002\u7b2c\u4e00\u90e8\u5206\u4e3b\u8981\u4ecb\u7ecd\u7684\u662f\u65e0\u6a21\u578b\u7684\u56e0\u679c\u63a8\u65ad\uff1b\u800c\u7b2c\u4e8c\u90e8\u5206\u5219\u4ecb\u7ecd\u6709\u6a21\u578b\u7684\u56e0\u679c\u63a8\u65ad\uff1b\u5728\u7b2c\u4e09\u90e8\u5206\u4e2d\uff0c\u5219\u628a\u7b14\u58a8\u7684\u91cd\u70b9\u653e\u5728\u4e86\uff1a\u5982\u4f55\u5728\u590d\u6742\u7684\u7eb5\u5411\u6570\u636e\u4e2d\u8fdb\u884c\u56e0\u679c\u63a8\u65ad\u3002\u5e76\u628a\u9002\u7528\u4e8e\u6240\u6709\u8bfb\u8005\u7684\u7591\u96be\u70b9\uff08Fine Points\uff09\u548c\u9488\u5bf9\u4e13\u4e1a\u4eba\u58eb\u7684\u6280\u672f\u70b9\uff08Technical points\uff09\u90fd\u7a7f\u63d2\u5728\u884c\u6587\u4e2d\u3002 \u6b63\u5982\u4f5c\u8005\u6240\u8a00\uff1a\u76ee\u524d\u73b0\u6709\u7684\u56e0\u679c\u63a8\u65ad\u8d44\u6599\u5927\u591a\u662f\u6563\u843d\u5728\u5b66\u672f\u671f\u520a\u4e0a\u7684\u6587\u7ae0\u3002\u4f5c\u8005\u5e0c\u671b\u8fd9\u672c\u4e66\u53ef\u4ee5\u5e2e\u52a9\u5230\u6240\u6709\u5bf9\u56e0\u679c\u63a8\u65ad\u611f\u5174\u8da3\u7684\u8bfb\u8005\uff0c\u7279\u522b\u662f\u5e0c\u671b\u793e\u4f1a\u79d1\u5b66\u548c\u5065\u5eb7\u9886\u57df\u7684\u4eba\u58eb\u80fd\u591f\u4ece\u4e2d\u53d7\u76ca\u3002 \u4e3b\u8981\u76ee\u5f55\u7ae0\u8282 Introduction: Towards less casual causal inferences I Causal inference without models1 A definition of causal effect2 Randomized experiments3 Observational studies4 Effect modification5 Interaction6 Graphical representation of causal effects7 Confounding8 Selection bias9 Measurement bias10 Random variability
II Causal inference with models11 Why model?12 IP weighting and marginal structural models13 Standardization and the parametric g-formula14 G-estimation of structural nested models15 Outcome regression and propensity scores16 Instrumental variable estimation17 Causal survival analysis18 Variable selection for causal inference
III Causal inference from complex longitudinal data19 Time-varying treatments20 Treatment-confounder feedback21 G-methods for time-varying treatments22 Target trial emulationReferences \u63a8\u8350\u9605\u8bfb\u4ec0\u4e48\u662f\u56e0\u679c\u63a8\u65ad | \u96c6\u667a\u767e\u79d1<\/a><\/p>\n

\u501f\u52a9\u56e0\u679c\u63a8\u65ad\uff0c\u66f4\u9c81\u68d2\u7684\u673a\u5668\u5b66\u4e60\u6765\u4e86\uff01<\/a><\/p>\n

\u5f20\u6c5f\uff1a\u4ece\u56fe\u7f51\u7edc\u5230\u56e0\u679c\u63a8\u65ad\uff0c\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u4e94\u90e8\u66f2<\/a><\/p>\n

 \u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u4e0b\u8f7dPDF\u539f\u6587 <\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

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