Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247592 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
This paper extends the evaluation of direct and indirect treatment effects, i.e., mediation analysis, to the case that outcomes are only partially observed due to sample selection or outcome attrition. We assume sequential conditional independence of the treatment and the mediator, i.e., the variable through which the indirect effect operates. We also impose missing at random or instrumental variable assumptions on the outcome attrition process. Under these conditions, we derive identification results for the effects of interest that are based on inverse probability weighting by specific treatment, mediator, and/or selection propensity scores. We also provide a simulation study and an empirical application to the U.S. Project STAR data in which we assess the direct impact and indirect effect (via absenteeism) of smaller kindergarten classes on math test scores. The estimators considered are available in the "causalweight" package for the statistical software "R".
Subjects: 
attrition
causal channels
causal mechanisms
causal pathways
direct effects
indirect effects
inverse probability weighting
mediation analysis
outcome nonresponse
propensity score
sample selection
JEL: 
C21
I21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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