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

• URL: http://arxiv.org/abs/2001.09887v4
• Date: Mon, 5 Sep 2022 14:16:11 GMT
• Title: Estimating heterogeneous treatment effects with right-censored data via causal survival forests
• Authors: Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu
• Abstract summary: We introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting. Our approach relies on estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness.
• Score: 2.624902795082451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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