Combating unmeasured confounding in cross-sectional studies: evaluating instrumental-variable and Heckman selection models.

ABSTRACT: Unmeasured confounding is the principal threat to unbiased estimation of treatment "effects" (i.e., regression parameters for binary regressors) in nonexperimental research. It refers to unmeasured characteristics of individuals that lead them both to be in a particular "treatment" category and to register higher or lower values than others on a response variable. In this article, I introduce readers to 2 econometric techniques designed to control the problem, with a particular emphasis on the Heckman selection model (HSM). Both techniques can be used with only cross-sectional data. Using a Monte Carlo experiment, I compare the performance of instrumental-variable regression (IVR) and HSM to that of ordinary least squares (OLS) under conditions with treatment and unmeasured confounding bot

SUBMITTER: DeMaris A 

PROVIDER: S-EPMC4217491 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature