协变量选择用于因果推断的双稳健双/去偏机器学习估计器
Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
- Department of Human Development and Quantitative Methodology, University of Maryland（马里兰大学人类发展与定量方法系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对高维数据下因果推断中协变量选择问题，提出使用倾向得分与结果模型所选协变量的并集重新估计双稳健模型，模拟显示该方法能更有效减少混杂偏差，并揭示ML估计并非总是优于传统方法。
AI中文摘要：
高维数据为因果效应估计带来了挑战，因为识别正确模型设定所需的协变量变得越来越困难。双/去偏机器学习（DML）通过减轻正则化和过拟合偏差，促进了机器学习（ML）在因果推断中的应用，但相比之下，关于协变量选择与某些DML估计器所具有的双稳健（DR）性质之间的关系，受到的关注较少。特别是，基于ML的协变量选择可能导致差异化的协变量选择，或导致两个模型均被错误设定，从而限制了DR性质的实际效用。为解决这些问题，我们提出使用倾向得分（PS）和结果ML模型所选协变量的并集来重新估计这两个模型。模拟结果表明，使用并集比使用单独选择的协变量集能更一致地减少混杂偏差。结果还表明，即使在有利于Lasso的条件下，基于ML的估计也并不总是优于传统的DR估计，并且后Lasso比标准Lasso能减少更多的混杂偏差。这些发现表明，ML在因果推断中的成功应用不仅取决于ML算法，还取决于如何将通过协变量选择获得的信息纳入因果效应估计中。
英文摘要：
High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facilitates the use of machine learning (ML) for causal inference by mitigating regularization and overfitting bias, but comparatively less attention has been given to covariate selection in relation to the double robustness (DR) property possessed by some DML estimators. In particular, ML-based covariate selection may result in differential covariate selection or in misspecification of both models, thereby limiting the practical utility of the DR property. To address these issues, we propose using the union of the covariates selected by the propensity score (PS) and outcome ML models to re-estimate both models. Simulation results show that using the union consistently reduces more confounding bias than using separate selected covariate sets. The results also show that ML-based estimation does not uniformly outperform conventional DR estimation, even under conditions favorable to the Lasso, and that post-Lasso reduces more confounding bias than standard Lasso. These findings demonstrate that successful use of ML for causal inference depends not only on the ML algorithm but also on how the information obtained through covariate selection is incorporated into causal effect estimation.
