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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Veröffentlicht in:	Journal of the American Statistical Association Jg. 113; H. 523; S. 1228 - 1242
Hauptverfasser:	Wager, Stefan, Athey, Susan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 03.07.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	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-Zugang:	Volltext
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