Identifying effects of multivalued treatments

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP34/18

Classification and Topics

• Classification: Wirtschaft
• Subject: Identification ; selection ; multivalued treatments ; instruments ; monotonicity ; multidimensional unobserved heterogeneity

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Lee, Sokbae ; Salanié, Bernard
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2018

Further information

• DOI: doi:10.1920/wp.cem.2018.3418
• Handle: http://hdl.handle.net/10419/189749
• Last update: 10.03.2025, 11:42 AM CET