Sensitivity analysis for average treatment effects
Sascha O. Becker
Center for Economic Studies
Ludwig-Maximilians-University
Munich, Germany
[email protected]
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Marco Caliendo
German Institute for Economic Research (DIW)
Berlin, Germany
[email protected]
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Abstract. Based on the conditional independence or unconfoundedness assumption,
matching has become a popular approach to estimate average treatment
effects. Checking the sensitivity of the estimated results with respect to
deviations from this identifying assumption has become an increasingly
important topic in the applied evaluation literature. If there are
unobserved variables that affect assignment into treatment and the outcome
variable simultaneously, a hidden bias might arise to which matching
estimators are not robust. We address this problem with the bounding
approach proposed by Rosenbaum (Observational Studies, 2nd ed., New
York: Springer), where mhbounds lets the researcher determine how
strongly an unmeasured variable must influence the selection process to
undermine the implications of the matching analysis.
View all articles by these authors:
Sascha O. Becker, Marco Caliendo
View all articles with these keywords:
mhbounds, matching, treatment effects, sensitivity analysis, unobserved heterogeneity, Rosenbaum bounds
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