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

2025-07-11 health informatics Title + abstract only

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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) of each feature on clinical outcomes while adjusting for confounders. We evaluate...

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

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