IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING
Article first published online: 30 JUN 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 29, Issue 6, pages 920–943, September/October 2014
How to Cite
2014), IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING, J. Appl. Econ., 29, 920–943, doi: 10.1002/jae.2341(
- Issue published online: 8 OCT 2014
- Article first published online: 30 JUN 2013
- Manuscript Revised: 16 MAR 2013
- Manuscript Received: 5 JUL 2012
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 US Job Corps program is also provided. Copyright © 2013 John Wiley & Sons, Ltd.