Predicting and Estimating Treatment Effects in Clinical Decision Support Systems

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Predicting and Estimating Treatment Effects in Clinical Decision Support Systems

A Causal Machine Learning Framework for Predicting Preventable Hospital Readmissions

Predictive algorithms are increasingly being used to form the basisfor optimal treatment policies, which is to enable interventions to be targetedto the patients who will presumably benefit most.

We introduce a causal machine learning framework that decouples this prediction problem into causal and predictive parts, which clearly delineates the complementary roles of causal inference and prediction in this problem.

We estimate treatment effects using causal forests, and characterize treatment effect heterogeneity across levels of predicted risk using these estimates.

Authors

Ben J. Marafino, Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar, Mike Baiocchi

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