Cited by 1,586Advanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods in Clinical Trials

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Recursive partitioning for heterogeneous causal effects

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Athey, Susan, Imbens, Guido·2016·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1510489113PMC PMC4941430

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Abstract

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.

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Authors: Athey, S., & Imbens, G.
Affiliations: Stanford University (US)
ORCID: Athey, Susan
Imbens, Guido
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2016-07-05
Volume: 113
Issue: 27
Pages: 7353-7360
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1510489113
PMC: PMC4941430
OpenAlex: W2305754340
Cited by: 1,586
References: 43
Citations by year: 2026: 213 · 2025: 235 · 2024: 198 · 2023: 175 · 2022: 170 · 2021: 178 · 2020: 161 · 2019: 110 · 2018: 66 · 2017: 61 · 2016: 15
FWCI: 87.13
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.pnas.org/preview_site/misc/userlicense.xhtml (vor, 184 d)
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Deep Blue (University of Michigan) (submittedVersion)
Funders: Microsoft Research (0)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: treatment effect heterogeneity, recursive partitioning, regression trees, cross-validation, model selection criteria, high-dimensional covariates, causal inference
MeSH terms: Machine Learning, Computer Simulation, Models, Theoretical, Research Design, Statistics as Topic

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Recursive partitioning for heterogeneous causal effects

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