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Title: Estimating heterogeneous treatment effects with right-censored data via causal survival forests Authors:  Yifan Cui - Zhejiang University (China) [presenting]
Michael Kosorok - University of North Carolina at Chapel Hill (United States)
Erik Sverdrup - Standford University (United States)
Stefan Wager - Stanford University (United States)
Ruoqing Zhu - University of Illinois at Urbana-Champaign (United States)
Abstract: Forest-based methods have recently gained popularity for non-parametric treatment effect estimation. We introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational settings where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.