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Identifying causal mechanisms (primarily) based on inverse probability weighting

abstract This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting. I.e., we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the (unmediated) direct effect. Even under random treatment assignment, subsequent selection into the mediator is generally non-random such that causal mechanisms are only identified when controlling for confounders of the mediator and the outcome. To tackle this issue, units are weighted by the inverse of their conditional treatment propensity given the mediator and observed confounders. We show that the form and applicability of weighting depend on whether some confounders are themselves influenced by the treatment or not. A simulation study gives the intuition for these results and an empirical application to the direct and indirect health effects (through employment) of the U.S. Job Corps program is also provided.
   
type journal paper
   
keywords causal mechanisms, mediation analysis, direct and indirect effects, inverse probability weighting.
   
project Identifying direct and indirect effects in economics
language English
kind of paper journal article
date of appearance 30-6-2013
journal Journal of Applied Econometrics
publisher Wiley-Blackwell (Chichester UK)
ISSN 0883-7252
ISSN (online) 1099-1255
DOI 10.1002/jae.2341
volume of journal 2013
number of issue early view seit 06.13
page(s) 1-24
review double-blind review
   
citation Huber, M. (2013). Identifying causal mechanisms (primarily) based on inverse probability weighting. Journal of Applied Econometrics, 2013(early view seit 06.13), 1-24, DOI:10.1002/jae.2341.