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

Yifan Cui; Michael R. Kosorok; Erik Sverdrup; Stefan Wager; Ruoqing Zhu

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arXiv · 2001.09887

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

Yifan Cui·Michael R. Kosorok·Erik Sverdrup·Stefan Wager·Ruoqing Zhu

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Abstract

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 under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.

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Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu. 2020-01-27. Estimating heterogeneous treatment effects with right-censored data via causal survival forests. https://arxiv.org/abs/2001.09887

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