Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
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因果推断中双重稳健双重／去偏机器学习估计的协变量选择
- 研究问题
- 倾向得分与结局模型分别选择协变量时，如何避免同时误设削弱双重稳健性，并改善高维因果效应估计中的混杂偏差？
- 方法与思路
- 作者取倾向得分和结局机器学习模型所选变量的并集，以该共同变量集重新估计两模型，并通过模拟比较不同估计策略。
- 材料陈述的贡献
- 摘要称，模拟中并集策略比分别选变量更能减少混杂偏差；机器学习并非始终优于传统双重稳健估计，后 Lasso 也有优势。
- 阅读关注点
- 应核对模拟中的混杂与模型误设方式、变量筛选和重估如何嵌入 DML，以及偏差改善是否伴随方差或覆盖率变化。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要主要提供模拟证据，未说明并集策略具有普遍理论保证。
原文摘要
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.