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Identifying Effects of Multivalued Treatments

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  • Sokbae Lee
  • Bernard Salani'e
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.

Suggested Citation

  • Sokbae Lee & Bernard Salani'e, 2018. "Identifying Effects of Multivalued Treatments," Papers 1805.00057, arXiv.org.
    • Handle: RePEc:arx:papers:1805.00057
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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