Identifying causal mechanisms (primarily) based on inverse probability weighting
Journal
Journal of Applied Econometrics
ISSN
0883-7252
ISSN-Digital
1099-1255
Type
journal article
Date Issued
2013-06-30
Author(s)
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.
Language
English
Keywords
causal mechanisms
mediation analysis
direct and indirect effects
inverse probability weighting.
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Wiley-Blackwell
Publisher place
Chichester UK
Volume
2013
Number
early view seit 06.13
Start page
1
End page
24
Pages
24
Subject(s)
Division(s)
Eprints ID
210019
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