Estimating heterogeneous treatment effects with right-censored data via causal survival forests
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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.
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Advanced ML techniques, such as causal forests, can directly estimate heterogeneous treatment effects and minimize bias in observational studies.
“Additionally, advanced ML techniques, such as causal forests, can directly estimate heterogeneous treatment effects and minimize bias in observational studies [37–39].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [38]
ML and AI techniques, such as survival analysis (e.g. Survival Forest) and Bayesian networks, can model the time-varying effects of exposures on health outcomes and identify optimal timing and targeting of interventions.
“ML and AI techniques, such as survival analysis (e.g. Survival Forest) and Bayesian networks, can model the time-varying effects of exposures on health outcomes and identify optimal timing and targeting of interventions [37, 38].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [38]
Causal inference methods enhanced by ML, such as causal forests, can simultaneously address multiple principles by estimating heterogeneous treatment effects across different life stages, social contexts, and individual characteristics.
“For instance, causal inference methods enhanced by ML, such as causal forests, can simultaneously address multiple principles by estimating heterogeneous treatment effects across different life stages, social contexts, and individual characteristics [37–39].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [38]
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- Authors
- Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R.
- Affiliations
- Zhejiang University (CN)
- University of North Carolina at Chapel Hill (US)
- Stanford University (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Cui, Yifan
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-02-27
- Volume
- 85
- Issue
- 2
- Pages
- 179-211
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W4322397527
- Cited by
- 117
- References
- 73
- Citations by year
- 2026: 32 · 2025: 36 · 2024: 25 · 2023: 10 · 2022: 6 · 2021: 6 · 2020: 2
- FWCI
- 21.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- National Science Foundation
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- treatment effect heterogeneity, right-censored data, survival analysis, selection effects, unconfoundedness
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