Article

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine Learning

2025-07-11

Abstract excerpt

High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causally relevant variables, compromising model interpretability and clinical utility. We introduce CausalDRIFT, a causal feature selection algorithm grounded in the Frisch-Waugh-Lovell theorem and Double Machine Learning, which estimates the Average Treatment Effect (ATE...

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Literature Corpus work: a4e8b858-7e54-54c0-83f8-427a3950f805
DOI: 10.1101/2025.07.10.25331298

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