Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246809 
Year of Publication: 
2021
Series/Report no.: 
cemmap working paper No. CWP41/21
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
Subjects: 
Treatment effect
Multiple treatments
Heterogeneous coefficients
Control variable
Identification
Conditional nonsingularity
Propensity score
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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