{"id":33211,"date":"2022-03-19T21:47:22","date_gmt":"2022-03-19T13:47:22","guid":{"rendered":"https:\/\/swarma.org\/?p=33211"},"modified":"2022-03-19T21:47:22","modified_gmt":"2022-03-19T13:47:22","slug":"%e7%bc%ba%e5%a4%b1%e6%95%b0%e6%8d%ae%e5%92%8c%e5%9b%a0%e6%9e%9c%e6%8e%a8%e6%96%ad%e4%b8%ad%e7%9a%84%e5%8f%8c%e7%a8%b3%e5%81%a5%e6%96%b9%e6%b3%95%e4%bb%8b%e7%bb%8d-%e5%91%a8%e6%97%a5%e7%9b%b4","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=33211","title":{"rendered":"\u7f3a\u5931\u6570\u636e\u548c\u56e0\u679c\u63a8\u65ad\u4e2d\u7684\u53cc\u7a33\u5065\u65b9\u6cd5\u4ecb\u7ecd | \u5468\u65e5\u76f4\u64ad\u00b7\u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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    Scharfstein, D. O., Rotnitzky, A. & Robins, J. M. (1999), “Adjusting for nonignorable drop-out using semiparametric nonresponse models”, Journal of American Statistical Association 94, 1096-1120.<\/span><\/section>\n<\/li>\n
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    Bang, H. & Robins, J. (2005), “Doubly robust estimation in missing data and causal inference models”, Biometrics 61, 962-973.<\/span><\/section>\n<\/li>\n
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    Kang, J. D. & Schafer, J. L. (2007), “Demystifying double robustness: a comparison of lternative strategies for estimating a population mean from incomplete data”, Statistical Science 22, 523-539.<\/span><\/section>\n<\/li>\n
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    Mark J. van der Laan & Sherri Rose (2011). “Targeted Learning: Causal Inference for Observational and Experimental Data”. Springer.<\/span><\/section>\n<\/li>\n
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    Robins, J., Sued, M., Lei-Gomez, Q. & Rotnitzky, A. (2007), “Comment: Performance of double-robust estimators when inverse probability weights are highly variable”, Statistical Science 22, 544-559.<\/span><\/section>\n<\/li>\n
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    Tan, Z. (2006a), “A distributional approach for causal inference using propensity scores”, Journal of the American Statistical Association 101(476), 1619-1637.<\/span><\/section>\n<\/li>\n
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    Peng Wu, Zhiqiang Tan, Wenjie Hu & Xiao-Hua Zhou (2021), ‘Model-assisted inference for covariate-Specic treatment effects with high-dimensional data’, arXiv:2105.11362.<\/span><\/section>\n<\/li>\n<\/ol>\n


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