Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

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 Brei...

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Bibliographic Details
Published in:Journal of the American Statistical Association Vol. 113; no. 523; pp. 1228 - 1242
Main Authors: Wager, Stefan, Athey, Susan
Format: Journal Article
Language:English
Published: Alexandria Taylor & Francis 03.07.2018
Taylor & Francis Group,LLC
Taylor & Francis Ltd
Subjects:
Adaptive nearest neighbors matching
Algorithms
Asymptotic methods
Asymptotic normality
Asymptotic properties
Causality
Classification
confidence interval
Confidence intervals
equations
Estimation
Experiments
forests
Heterogeneity
Inference
Marketing
Medicine
Potential outcomes
Precision medicine
Regression analysis
Sampling
Statistical analysis
Statistical inference
Statistical methods
Statistics
Theory and Methods
Unconfoundedness
ISSN:0162-1459, 1537-274X, 1537-274X
Online Access:Get full text
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Summary: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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ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2017.1319839