Arbeitspapier
Identification and estimation of causal mechanisms and net effects of a treatment under unconfoundedness
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 4237
- Klassifikation
-
Sozialwissenschaften, Soziologie, Anthropologie
Estimation: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Semiparametric and Nonparametric Methods: General
- Thema
-
Causal inference
causal mechanisms
post-treatment variables
principal stratification
Statistische Bestandsanalyse
Kausalanalyse
Dekompositionsverfahren
Theorie
Schätzung
USA
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Flores, Carlos A.
Flores-Lagunes, Alfonso
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for the Study of Labor (IZA)
- (wo)
-
Bonn
- (wann)
-
2009
- Handle
- URN
-
urn:nbn:de:101:1-20090622213
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Flores, Carlos A.
- Flores-Lagunes, Alfonso
- Institute for the Study of Labor (IZA)
Entstanden
- 2009