Recursive partitioning for heterogeneous causal effects
Susan Athey,Guido W. Imbens +1 more
Reads0
Chats0
TLDR
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.Abstract:
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.read more
Citations
More filters
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
Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
TL;DR: This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.
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.
Journal ArticleDOI
The State of Applied Econometrics: Causality and Policy Evaluation
Susan Athey,Guido W. Imbens +1 more
TL;DR: In this paper, the authors discuss recent developments in econometrics that they view as important for empirical researchers working on policy evaluation questions, focusing on three main areas, where in each case they highlight recommendations for applied work.
Journal ArticleDOI
Metalearners for estimating heterogeneous treatment effects using machine learning
TL;DR: A metalearner, the X-learner, is proposed, which can adapt to structural properties, such as the smoothness and sparsity of the underlying treatment effect, and is shown to be easy to use and to produce results that are interpretable.
References
More filters
Journal ArticleDOI
Efficient estimation of average treatment effects using the estimated propensity score
TL;DR: The authors showed that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score leads to an efficient estimation of the average treatment effect.
Book
Counterfactuals and Causal Inference: Methods and Principles for Social Research
TL;DR: In this article, the authors proposed a method to estimate causal effects by conditioning on observed variables to block backdoor paths in observational social science research, but the method is limited to the case of causal exposure and identification criteria for conditioning estimators.
Book
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Guido W. Imbens,Donald B. Rubin +1 more
TL;DR: In this paper, two world-renowned experts present statistical methods for studying causal in nature: what would happen to individuals, or to groups, if part of their environment were changed?
Book
Targeted Learning: Causal Inference for Observational and Experimental Data
M. J. van der Laan,Sherri Rose +1 more
TL;DR: This work focuses on TMLE in Adaptive Group Sequential Covariate Adjusted RCTs, which involves cross-Validated Targeted Minimum-Loss-Based Estimation and targeted Bayesian Learning.
Related Papers (5)
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more