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stat.ME2025
Multi-Study Causal Forest (MCF): A flexible framework for data borrowing in the presence of varying treatment effect heterogeneity
Ashwini Venkatasubramaniam, Julian Wolfson
Tailoring treatment assignment to specific individuals can improve the health outcomes, but a single study may offer inadequate information for this purpose. The ability to leverag…
stat.ME2019
Assessing effect heterogeneity of a randomized treatment using conditional inference trees
Ashwini Venkatasubramaniam, Brandon Koch, Lauren Erickson +3
Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and c…