Variable selection for doubly robust causal inference.

IF 0.7 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Statistics and Its Interface Pub Date : 2025-01-01 Epub Date: 2024-10-22 DOI:10.4310/sii.241023040813
Eunah Cho, Shu Yang
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引用次数: 0

Abstract

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.

双稳健因果推理的变量选择。
对于基于观察性研究的因果推理,混淆控制是至关重要的,但也是具有挑战性的。在典型的无混杂假设下,增广逆概率加权(AIPW)由于其双重稳健性而被广泛用于估计平均因果效应(ACE),即它既依赖于倾向得分模型,也依赖于结果均值模型来正确指定。为了确保关键假设成立,通常需要努力收集足够丰富的预处理变量集,从而使变量选择势在必行。众所周知,为了准确预测倾向性评分的变量选择可能会通过包括工具变量来产生变量ACE估计量。因此,许多最近的工作建议选择所有的结果预测因子,以进行混淆控制和有效估计。本文表明,以有效估计为目标的变量选择AIPW估计器可能会失去理想的双鲁棒性。相反,我们建议控制任何协变量的倾向评分模型,这些协变量是治疗或结果或两者的预测因子,这保留了AIPW估计器的双重稳健性。利用这一原理,我们提出了一个两阶段的过程,其中变量选择的惩罚和估计的AIPW估计器。结果表明,该方法具有良好的双鲁棒性。通过仿真和应用,对AIPW估计器在不同变量选择条件下的有限样本性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics and Its Interface
Statistics and Its Interface MATHEMATICAL & COMPUTATIONAL BIOLOGY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
0.90
自引率
12.50%
发文量
45
审稿时长
6 months
期刊介绍: Exploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.
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