TY  - RPRT
        AU  - Flores, Carlos A.
AU  - Flores-Lagunes, Alfonso
        TI  - Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness
        PY  - 2009/Jun/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 4237
        UR  - https://www.iza.org/publications/dp4237
        AB  - 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.
        KW  - causal inference
KW  - causal mechanisms
KW  - post-treatment variables
KW  - principal stratification
        ER  -