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Estimating Counterfactual Distributions under Interference
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:923-940, 2025.
Abstract
Randomized control trials (RCTs) form a key tool for evaluating medical treatments. However commonly used techniques in RCT based treatment effect estimates suffer from two issues: a) ignoring spillover effects and b) focusing on average (or conditional) average effects. This is partly because evaluating counterfactual distributions is hard; and is further complicated by presence of interference. In this work we propose a new estimator, named RUMI, for estimating distributional quantities like CVaR, QTE from controlled trials under known interference. We provide theoretical justification behind our method and demonstrate its application using synthetic experiments and real data.