Estimating treatment effect heterogeneity in randomized program evaluation
Kosuke Imai,Marc Ratkovic +1 more
TLDR
In this article, the authors proposed a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest.Abstract:
When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers must investigate when the treatments do and do not work. Indeed, the estimation of treatment effect heterogeneity plays an essential role in (1) selecting the most effective treatment from a large number of available treatments, (2) ascertaining subpopulations for which a treatment is effective or harmful, (3) designing individualized optimal treatment regimes, (4) testing for the existence or lack of heterogeneous treatment effects, and (5) generalizing causal effect estimates obtained from an experimental sample to a target population. In this paper, we formulate the estimation of heterogeneous treatment effects as a variable selection problem. We propose a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. The proposed method is motivated by and applied to two well-known randomized evaluation studies in the social sciences. Our method selects the most effective voter mobilization strategies from a large number of alternative strategies, and it also identifies the characteristics of workers who greatly benefit from (or are negatively affected by) a job training program. In our simulation studies, we find that the proposed method often outperforms some commonly used alternatives.read more
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ReportDOI
Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov,Denis Chetverikov,Mert Demirer,Esther Duflo,Christian Hansen,Whitney K. Newey,James M. Robins +6 more
TL;DR: In this article, the authors show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ0 can be removed by using two simple, yet critical, ingredients: (1) using Neyman-orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters, and (2) making use of cross-fitting, which provides an efficient form of data-splitting.
Journal ArticleDOI
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more
TL;DR: This paper developed a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm, and showed that causal forests are pointwise consistent for the true treatment effect and have an asymptotically Gaussian and centered sampling distribution.
Journal ArticleDOI
Covariate balancing propensity score.
Kosuke Imai,Marc Ratkovic +1 more
TL;DR: Covariate balancing propensity score (CBPS) as mentioned in this paper was proposed to improve the empirical performance of propensity score matching and weighting methods by exploiting the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment.
Journal ArticleDOI
Recursive partitioning for heterogeneous causal effects
Susan Athey,Guido W. Imbens +1 more
TL;DR: This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.
Posted Content
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more
TL;DR: 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 and is found to be substantially more powerful than classical methods based on nearest-neighbor matching.
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
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