Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Wager, Stefan; Athey, Susan

doi:10.1080/01621459.2017.1319839

Cited by 2,917Advanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods in Clinical Trials

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Wager, Stefan, Athey, Susan·2018·Journal of the American Statistical Association

DOI 10.1080/01621459.2017.1319839

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Abstract

Many scientific and engineering challenges—ranging from personalized medicine to customized marketing recommendations—require an understanding of treatment effect heterogeneity. In this article, we develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman’s widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.

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Recent research has shown that data-adaptive/machine-learning procedures can be used to more flexibly estimate treatment effect heterogeneity in RCTs or cross-sectional observational studies with unconfoundedness.
“recent research has shown that data-adaptive/machine-learning procedures can be used to more flexibly estimate treatment effect heterogeneity in the context of RCTs or cross-sectional observational studies with unconfoundedness”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Wager and Athey, 2018
Athey and Imbens (2016), Wager and Athey (2018), and Athey et al. (2019) developed random forests methods designed to capture honest heterogeneity in treatment effects, circumventing overfitting issues inherent to traditional methods and thus avoiding mistaking statistical noise for heterogeneity.
“In particular, Athey and Imbens (2016), Wager and Athey (2018), and Athey et al. (2019) developed random forests methods designed to capture honest heterogeneity in treatment effects, circumventing overfitting issues inherent to traditional methods and thus avoiding mistaking statistical noise for heterogeneity.”
From Prison Rehabilitation Programs: Efficiency and Targeting (2021) · cites this paper as Wager and Athey (2018)

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Authors: Wager, S., & Athey, S.
Affiliations: Stanford University (US)
ORCID: Wager, Stefan
Athey, Susan
Venue: Journal of the American Statistical Association
Publisher: Informa UK Limited
Published: 2018-06-06
Volume: 113
Issue: 523
Pages: 1228-1242
ISSN: 0162-1459 (ISSN-L)
1537-274X
DOI: 10.1080/01621459.2017.1319839
OpenAlex: W2208550830
Cited by: 2,917
References: 81
Citations by year: 2026: 486 · 2025: 490 · 2024: 398 · 2023: 328 · 2022: 298 · 2021: 326 · 2020: 254 · 2019: 191 · 2018: 104
FWCI: 110.49
Type: journal article
Language: en
Open access: closed
Locations: Journal of the American Statistical Association (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
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, causal forest, random forest, nonparametric causal inference, potential outcomes framework, unconfoundedness, asymptotic normality, pointwise consistency, asymptotic confidence intervals, nearest neighbor matching

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

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