Estimating heterogeneous treatment effects with right-censored data via causal survival forests

27 Jan 2020  ·  Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu ·

Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting where outcomes may be right-censored... Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects. In our experiments, we find our approach to perform well relative to a number of baselines. read more

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