Iterative Causal Forest: A Novel Algorithm for Subgroup Identification
American Journal of Epidemiology, 2024
We've developed a machine learning subgrouping algorithm based on causal forest to better identify subpopulation who will benefit (or be harmed) more from a specific treatment. The iCF algorithm iteratively develops shallow causal forests at different depths to obtain a family of subgroup decisions, then selects the cross-validated decision that best predicts causal treatment effect.