Identifying effects of multivalued treatments - Sokbae Lee; Bernard Salanié (Centre for Microdata Methods and Practice (cemmap), 2018)
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.
Keywords: 330; Identification; selection; multivalued treatments; instruments; monotonicity; multidimensional unobserved heterogeneity