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 Semiparametric Estimation of Treatment Effects in Randomized Experiments

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

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.

Efficient semiparametric estimation for Gini inequality treatment effects

2017 ◽  
Vol 154 ◽  
pp. 96-100 ◽  
Author(s):  
Xiaofeng Lv ◽  
Rui Li ◽  
Zheng Fang
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation
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