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Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

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

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