Open AccessPosted Content
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more
TLDR
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.Abstract:
Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric 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.read more
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Concrete Problems in AI Safety
TL;DR: A list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function, an objective function that is too expensive to evaluate frequently, or undesirable behavior during the learning process, are presented.
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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.
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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.
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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.
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Prediction Policy Problems.
TL;DR: This work argues an important class of policy problems does not require causal inference but instead requires predictive inference, and that new developments in the field of "machine learning" are particularly useful for addressing these prediction problems.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
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Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.