Profiling Compliers and Noncompliers for Instrumental-Variable Analysis

Bibliographic Data

ID7971103
AuthorsMoritz Marbach (0000-0002-7101-2821, ETH Zurich), Dominik Hangartner (0000-0002-4511-7507, ETH Zurich, corresponding author)
Year2020
Volume28
Issue3
Pages435-444
Publication date2020-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePolitical Analysis (JOURNAL)
Journal identifiersISSN: 1047-1987 • E-ISSN: 1476-4989
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/pan.2019.48
OpenAlexW2995809273
LanguageEN
Citations received30
References cited26

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by noncompliance or leverage partially exogenous treatment variation in observational studies. The potential outcome framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE) and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and noncompliers. Yet, profiling compliers and noncompliers is necessary to understand what subpopulation the researcher is making inferences about and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers, and never-takers in terms of their covariates and provide easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and noncompliers as a standard practice accompanying any IV analysis

Covariate · Data science · Econometrics · Estimator · Instrumental variable · Leverage (statistics · Machine learning · Observational study · Profiling (computer programming · Randomized experiment · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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