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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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“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]
In the citing paper
“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]
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“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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Tests for parameter instability and structural change with unknown change point.
Andrews, D. W. K. (1993) · Econometrica · doi:10.2307/2951764 · Cited by 4,334
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Maximum Likelihood Estimation of Misspecified Models
White, H. (1982) · Econometrica · doi:10.2307/1912526 · Cited by 4,207
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. K., & Robins, J. M. (2017) · Econometrics Journal · doi:10.1111/ectj.12097 · Cited by 3,595
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,943
Research works printed but not linked: 2
- Biau, 2012, Analysis of a random forests model, The Journal of Machine Learning Research, 13, 1063, 10.5555/2188385.2343682 · DOI
- Fleming, 2011, Counting processes and survival analysis(Vol. 169)
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Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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