scholarly journals Generic machine learning inference on heterogenous treatment effects in randomized experiments

2017 ◽  
Author(s):  
Mert Demirer ◽  
Esther Duflo ◽  
Ivan Fernandez-Val ◽  
Victor Chernozhukov
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Randomized Experiments

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

scholarly journals Metalearners for estimating heterogeneous treatment effects using machine learning

2019 ◽  
Vol 116 (10) ◽  
pp. 4156-4165 ◽  
Author(s):  
Sören R. Künzel ◽  
Jasjeet S. Sekhon ◽  
Peter J. Bickel ◽  
Bin Yu
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Field Experiments ◽  
Average Treatment Effect ◽  
Regularity Conditions ◽  
Heterogeneous Treatment Effects ◽  
Lipschitz Continuous ◽  
Additive Regression ◽  
Underlying Mechanisms ◽  
And Control

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

scholarly journals Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

2020 ◽  
pp. 107699862095198
Author(s):  
Youmi Suk ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Likelihood Estimation ◽  
Study Data ◽  
Latent Class Models ◽  
Finite Mixture ◽  
Latent Classes ◽  
Heterogeneous Treatment Effects

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

scholarly journals Semiparametric Estimation of Treatment Effects in Randomized Experiments

2021 ◽  
Author(s):  
Susan Carleton Athey ◽  
Peter Bickel ◽  
Aiyou Chen ◽  
Guido W. Imbens ◽  
Michael Pollmann
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation ◽  
Randomized Experiments

scholarly journals Estimating treatment effects with machine learning

2019 ◽  
Vol 54 (6) ◽  
pp. 1273-1282
Author(s):  
K. John McConnell ◽  
Stephan Lindner
Keyword(s):  
Machine Learning ◽  
Treatment Effects

Local Average and Quantile Treatment Effects Under Endogeneity: A Review

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Huber ◽  
Kaspar Wüthrich
Keyword(s):  
Instrumental Variable ◽  
Treatment Effects ◽  
Randomized Experiments ◽  
Quantile Treatment Effects ◽  
Indirect Treatment ◽  
Treatment Effect Models ◽  
Multiple Treatments ◽  
The Relationship ◽  
Local Average ◽  
Heterogeneous Treatment Effect

Abstract This paper provides a review of methodological advancements in the evaluation of heterogeneous treatment effect models based on instrumental variable (IV) methods. We focus on models that achieve identification by assuming monotonicity of the treatment in the IV and analyze local average and quantile treatment effects for the subpopulation of compliers. We start with a comprehensive discussion of the binary treatment and binary IV case as for instance relevant in randomized experiments with imperfect compliance. We then review extensions to identification and estimation with covariates, multi-valued and multiple treatments and instruments, outcome attrition and measurement error, and the identification of direct and indirect treatment effects, among others. We also discuss testable implications and possible relaxations of the IV assumptions, approaches to extrapolate from local to global treatment effects, and the relationship to other IV approaches.

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