Augmented match weighted estimators for average treatment effects
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平均处理效应的增广匹配加权估计量
- 研究问题
- 如何结合倾向得分匹配与增广逆概率加权，在极端倾向得分下改善稳定性，同时保留双重稳健性和效率？
- 方法与思路
- 作者用匹配权重替代逆倾向权重，使匹配数 K 不再固定，并提出以因果目标无偏估计为锚的交叉验证选择法。
- 材料陈述的贡献
- 摘要称，AMW 具有双重稳健性，两类干扰模型均正确时可达半参数效率界，且可采用非参数 bootstrap 推断。
- 阅读关注点
- 应核对因果识别与重叠条件、K 的增长要求和选择准则，以及效率结论和 bootstrap 有效性的具体条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列出完整正则条件，稳定性不能解释为消除重叠问题。
原文摘要
Propensity score matching (PSM) and augmented inverse propensity weighting (AIPW) are widely used in observational studies to estimate causal effects. The two approaches present complementary features. The AIPW estimator is doubly robust and locally efficient but can be unstable when the propensity scores are close to zero or one due to weighting by the inverse of the propensity score. On the other hand, PSM circumvents the instability of propensity score weighting but it hinges on the correctness of the propensity score model and cannot attain the semiparametric efficiency bound. Besides, the fixed number of matches, K, renders PSM nonsmooth and thus invalidates standard nonparametric bootstrap inference. This article presents novel augmented match weighted (AMW) estimators that combine the advantages of matching and weighting estimators. AMW adheres to the form of AIPW for its double robustness and local efficiency but it mitigates the instability due to weighting. We replace inverse propensity weights with matching weights resulting from PSM with unfixed K. Meanwhile, we propose a new cross-validation procedure to select K that minimizes the mean squared error anchored around an unbiased estimator of the causal estimand. Besides, we derive the limiting distribution for the AMW estimators showing that they enjoy the double robustness property and can achieve the semiparametric efficiency bound if both nuisance models are correct. As a byproduct of unfixed K which smooths the AMW estimators, nonparametric bootstrap can be adopted for variance estimation and inference. Furthermore, simulation studies and real data applications support that the AMW estimators are stable with extreme propensity scores and their variances can be obtained by naive bootstrap.