Variable Selection for Doubly Robust Causal Inference
双重稳健因果推断中的变量选择AI 解读 ↓
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双重稳健因果推断中的变量选择
- 研究问题
- 观察性研究收集大量处理前变量后，如何选择协变量，使平均因果效应的 AIPW 估计兼顾混杂控制与双重稳健性？
- 方法与思路
- 作者主张倾向得分模型控制处理或结局的预测变量，并据此提出两阶段惩罚变量选择与 AIPW 效应估计程序。
- 材料陈述的贡献
- 摘要指出面向效率的变量选择可能破坏双重稳健性，称所提程序保留该性质，并通过模拟和应用评估有限样本表现。
- 阅读关注点
- 应核对变量选择破坏双重稳健性的机制、两阶段程序的模型设定与惩罚条件，以及工具变量对方差和稳健性的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要以无混杂假设为前提，未说明理论条件及模拟改善的具体幅度。
原文摘要
Confounding control is crucial and yet challenging for causal inference based on observational studies. Under the typical unconfoundness assumption, augmented inverse probability weighting (AIPW) has been popular for estimating the average causal effect (ACE) due to its double robustness in the sense it relies on either the propensity score model or the outcome mean model to be correctly specified. To ensure the key assumption holds, the effort is often made to collect a sufficiently rich set of pretreatment variables, rendering variable selection imperative. It is well known that variable selection for the propensity score targeted for accurate prediction may produce a variable ACE estimator by including the instrument variables. Thus, many recent works recommend selecting all outcome predictors for both confounding control and efficient estimation. This article shows that the AIPW estimator with variable selection targeted for efficient estimation may lose the desirable double robustness property. Instead, we propose controlling the propensity score model for any covariate that is a predictor of either the treatment or the outcome or both, which preserves the double robustness of the AIPW estimator. Using this principle, we propose a two-stage procedure with penalization for variable selection and the AIPW estimator for estimation. We show the proposed procedure benefits from the desirable double robustness property. We evaluate the finite-sample performance of the AIPW estimator with various variable selection criteria through simulation and an application.