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Identifying direct and indirect effects in economics
Type
fundamental research project
Start Date
01 September 2011
End Date
31 May 2012
Status
completed
Keywords
direct effects
indirect effects
causal mechanisms
endogeneity
attrition
Description
The identification of the causal effect of an explanatory variable on an outcome is a central task in economics. However, the (total) causal effect does usually not reveal the underlying causal mechanisms through which it operates. E.g., an active labor market policy might increase the labor market success (such as placement into employment) of participants directly by increasing the human capital, or indirectly through increasing job search effort, which is itself an intermediate outcome of the labor market policy. Though the identification of direct and indirect effects is crucial for understanding the causal mechanisms that characterize an economic problem, convincing identification strategies are rare. In particular the ubiquitous issues of endogeneity (of (i) the explanatory variable, (ii) the intermediate outcome, or (iii) both with the outcome of interest) and non-random outcome attrition (when the outcome is only observed for a selective subpopulation) have been widely ignored. This research project aims at developing credible non- and semiparametric methods for the identification of direct and indirect effects under endogeneity and/or outcome attrition. Both instrumental variable strategies for point identification and partial identification (i.e., the derivation of bounds on the effects in the absence of an instrument) will be used depending on the economic problem. Furthermore, the methods will be applied to empirical labor and health data. The project is to be realized in the course of a full year research visit to Prof. Guido W. Imbens, Ph.D., at Harvard University, one of the world's leading micro-econometricians.
Leader contributor(s)
Funder(s)
Topic(s)
econometric methods for the investigation of causal mechanisms in microeconomics
Method(s)
semi- and nonparametric microeconometrics
inverse probability weighting
Range
HSG Internal
Range (De)
HSG Intern
Division(s)
Eprints ID
200225
Reference Number
PBSGP1_138770
2 results
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PublicationDirect and indirect treatment effects: Causal chains and mediation analysis with instrumental variablesThis paper discusses the nonparametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment using distinct instruments for the endogenous treatment and the endogenous mediator. We examine different settings to obtain nonparametric identification of (natural) direct and indirect as well as controlled direct effects for continuous and discrete mediators and continuous and discrete instruments. We illustrate our approach in two applications: to disentangle the effects (i) of education on health, which may be mediated by income, and (ii) of the Job Corps training program, which may affect earnings indirectly via working longer hours and directly via higher wages per hour.Type: working paper
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PublicationIdentifying causal mechanisms (primarily) based on inverse probability weightingThis 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 articleJournal: Journal of Applied EconometricsVolume: 2013Issue: early view seit 06.13DOI: 10.1002/jae.2341
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