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arXiv · 1805.00057

Identifying Effects of Multivalued Treatments

Sokbae Lee·Bernard Salanié
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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.

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Sokbae Lee, Bernard Salanié. 2018-04-30. Identifying Effects of Multivalued Treatments. https://arxiv.org/abs/1805.00057

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