IDENTIFYING TREATMENT EFFECTS UNDER DATA COMBINATION

We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate data sets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are...

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Veröffentlicht in:	Econometrica 2014-03, Vol.82 (2), p.811-822
Hauptverfasser:	Fan, Yanqin, Sherman, Robert, Shum, Matthew
Format:	Artikel
Sprache:	eng
Schlagworte:	College attendance Contrafactuals Counterfactual distributions Covariance Data analysis Datasets Demography Distribution Distribution functions Econometrics Economic policy Health outcomes Inequality Inference NOTES AND COMMENTS Outcome variables Parameter identification partial identification Probability distribution Random variables Studies Tax payments treatment effects
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Zusammenfassung:	We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate data sets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone rearrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.
ISSN:	0012-9682 1468-0262
DOI:	10.3982/ECTA10601