A ROBUST AND EFFICIENT APPROACH TO CAUSAL INFERENCE BASED ON SPARSE SUFFICIENT DIMENSION REDUCTION.
A ROBUST AND EFFICIENT APPROACH TO CAUSAL INFERENCE BASED ON SPARSE SUFFICIENT DIMENSION REDUCTION.
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DOI：
10.1214/18-aos1722
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发表时间：
2019-02
影响因子：
4.5
通讯作者：
Shujie Ma;Liping Zhu;Zhiwei Zhang;Chih-Ling Tsai;R. Carroll
中科院分区：
文献类型：
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作者：
Shujie Ma;Liping Zhu;Zhiwei Zhang;Chih-Ling Tsai;R. Carroll
用观察数据进行因果推理时使用的一个基本假设是，给定测量的混杂变量，处理分配是可以忽略的。如果分析中包含大量基线协变量，那么没有缺失混杂因素的假设是合理的，因为我们通常不知道哪些变量可能是重要的混杂因素。因此，用大量协变量估计治疗效果近年来受到了相当大的关注。大多数现有的方法需要指定某些参数模型，包括结果、治疗和混杂变量，并采用变量选择程序来识别混杂因素。然而，选择一组合适的混杂因素取决于工作模型的正确规范。由于模型不规范和混淆变量的不正确选择导致的偏差可能产生误导性的结果。我们提出了一种鲁棒和有效的方法，通过灵活的建模策略结合惩罚变量选择来推断平均处理效果。具体来说，我们考虑一个基于有效影响函数构建的估计量，该函数包括倾向得分和结果回归。然后，我们提出了一种新的稀疏充分降维方法来估计这两个函数，而不做限制性参数建模假设。所提出的平均处理效果的估计量是渐近正态和半参数有效的，不需要变量选择的一致性。通过仿真研究和生物医学应用说明了所提出的方法。
A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most existing methods require specifying certain parametric models involving the outcome, treatment and confounding variables, and employ a variable selection procedure to identify confounders. However, selection of a proper set of confounders depends on correct specification of the working models. The bias due to model misspecification and incorrect selection of confounding variables can yield misleading results. We propose a robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection. Specifically, we consider an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression. We then propose a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions. The proposed estimator of the average treatment effect is asymptotically normal and semiparametrically efficient without the need for variable selection consistency. The proposed methods are illustrated via simulation studies and a biomedical application.