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

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.

arXiv commentTo appear in the Journal of the Royal Statistical Society, Series B

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• Analysis of a Random Forests Model
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Cited by in corpus (3)

• Enabling Counterfactual Survival Analysis with Balanced Representations
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
• Using public clinical trial reports to evaluate observational study methods