Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
利用高维经验似然进行平均处理效应的多重稳健推断AI 解读 ↓
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利用高维经验似然进行平均处理效应的多重稳健推断
- 研究问题
- 高维协变量与异质处理分配机制使单一倾向得分模型难以正确设定时，如何构造具有多重稳健性的平均处理效应置信区间？
- 方法与思路
- 作者以软协变量平衡约束下的高维经验似然组合多个倾向得分模型，并通过扩展校准函数与正则化增广结果回归修正偏差。
- 材料陈述的贡献
- 摘要称任一倾向模型、其线性组合或结果回归正确时，可实现渐近名义覆盖，并扩展至广义线性结果模型与未知群组场景。
- 阅读关注点
- 应核对多重稳健条件的精确定义、高维正则化要求、非精确平衡的偏差控制，以及估计群组如何影响正交性和区间覆盖。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未完整列出因果识别、高维增长及群组条件，模拟优势不能保证全面优于双重稳健方法。
原文摘要
In this paper, we develop a multiply robust inference procedure of the average treatment effect (ATE) for data with high-dimensional covariates. We consider the case where it is difficult to correctly specify a single parametric model for the propensity scores (PS). For example, the target population is formed from heterogeneous sources with different treatment assignment mechanisms. We propose a novel high-dimensional empirical likelihood weighting method under soft covariate balancing constraints to combine multiple working PS models. An extended set of calibration functions is used, and a regularized augmented outcome regression is developed to correct the bias due to non-exact covariate balancing. Those two approaches provide a new way to construct the Neyman orthogonal score of the ATE. The proposed confidence interval for the ATE achieves asymptotically valid nominal coverage under high-dimensional covariates if any of the PS models, their linear combination, or the outcome regression model is correctly specified. The proposed method is extended to generalized linear models for the outcome variable. Specifically, we consider estimating the ATE for data with unknown clusters, where multiple working PS models can be fitted based on the estimated clusters. Our proposed approach enables robust inference of the ATE for clustered data. We demonstrate the advantages of the proposed approach over the existing doubly robust inference methods under high-dimensional covariates via simulation studies. We analyzed the right heart catheterization dataset, initially collected from five medical centers and two different phases of studies, to demonstrate the effectiveness of the proposed method in practice.