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CWP341818.pdf
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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.
Authors
![Sokbae "Simon" Lee](/sites/default/files/styles/square_desktop/public/2022-07/Simon%20Lee.jpg?itok=J8PnQSbc)
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Professor of Economics Columbia
Working Paper details
- DOI
- 10.1920/wp.cem.2018.3418
- Publisher
- The IFS
Suggested citation
Lee, S and Salanie, B. (2018). Identifying effects of multivalued treatments. London: The IFS. Available at: https://ifs.org.uk/publications/identifying-effects-multivalued-treatments (accessed: 30 June 2024).
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