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

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

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish (US)
Pages (from-to)179-211
Number of pages33
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume85
Issue number2
DOIs
StatePublished - Apr 2023

Keywords

  • causal inference
  • censored data
  • heterogeneous treatment effects
  • machine learning
  • random forest
  • survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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