Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/35701
Authors: 
Flores, Carlos A.
Flores-Lagunes, Alfonso
Year of Publication: 
2009
Series/Report no.: 
IZA Discussion Papers 4237
Abstract: 
An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.
Subjects: 
Causal inference
causal mechanisms
post-treatment variables
principal stratification
JEL: 
C13
C21
C14
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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