Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189749 
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP34/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.
Subjects: 
Identification
selection
multivalued treatments
instruments
monotonicity
multidimensional unobserved heterogeneity
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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