Endogenous Stratification in Randomized Experiments (2014)
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BibTeX
@MISC{Abadie14endogenousstratification,
author = {Alberto Abadie and Matthew M. Chingos and Martin R. West},
title = {Endogenous Stratification in Randomized Experiments},
year = {2014}
}
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Abstract
Researchers and policy makers are often interested in estimating how treat-ments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an index of baseline characteristics that predicts the values that individual outcomes would take on in the absence of the treatment. This article shows that substantial biases may arise in practice if the index is estimated, as is often the case, by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We analyze the behavior of leave-one-out and repeated split sample estimators and show they behave well in realistic scenarios, correcting the large bias problem of the full sample estimator. We use data from the National JTPA Study and the Tennessee STAR experiment to demonstrate the performance of alternative estimators and the magnitude of their biases.
Keyphrases
endogenous stratification randomized experiment baseline characteristic large bias problem full sample full sample estimator policy maker experimental data experimental control split sample estimator realistic scenario alternative estimator common practice national jtpa study tennessee star experiment substantial bias outcome variable policy intervention individual outcome
