Journal of the American Statistical Association· 2017Q1
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- 2,812citations
- Q1SCImago
- 2017year
Short summary
A new 'causal forest' method, extending random forests, provides statistically valid inference for heterogeneous treatment effects, outperforming traditional methods especially with irrelevant variables.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a nonparametric causal forest for estimating heterogeneous treatment effects.
- Causal forests are shown to be pointwise consistent with asymptotically Gaussian sampling distributions.
- Introduced a practical method for constructing confidence intervals for treatment effects.
- This is the first method allowing provably valid statistical inference using any type of random forest.
- Causal forests demonstrated superior power compared to nearest-neighbor matching in experiments, especially with irrelevant covariates.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of the American Statistical Association, 2017 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics