Home  >>  Archives  >>  Volume 7 Number 1  >>  st0121

The Stata Journal
Volume 7 Number 1: pp. 71-83

Subscribe to the Stata Journal

Sensitivity analysis for average treatment effects

Sascha O. Becker
Center for Economic Studies
Munich, Germany
Marco Caliendo
German Institute for Economic Research (DIW)
Berlin, Germany
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.
Terms of use     View this article (PDF)

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

Download citation: BibTeX  RIS

Download citation and abstract: BibTeX  RIS