Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241920 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP45/20
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 generalized propensity scores (Imbens, 2000) are bounded away from zero with probability one, a simple identification condition is that their sum be bounded away from one with probability one. 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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