1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
visTree: Visualization of Subgroups for a Decision Tree
Ashwini Venkatasubramaniam, Julian Wolfson
Decision trees are flexible prediction models which are constructed to quantify outcome-covariate relationships and characterize relevant population subgroups. However, the standar…
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…