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Identification in Multiple Treatment Models under Discrete Variation

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  • Vishal Kamat
  • Samuel Norris
  • Matthew Pecenco

Abstract

We develop a method to learn about treatment effects in multiple treatment models with discrete-valued instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permits multidimensional unobserved heterogeneity. Under a semi-parametric restriction on the distribution of unobserved heterogeneity, we show how a sequence of linear programs can be used to compute sharp bounds for a number of treatment effect parameters when the marginal treatment response functions underlying them remain nonparametric or are additionally parameterized.

Suggested Citation

  • Vishal Kamat & Samuel Norris & Matthew Pecenco, 2023. "Identification in Multiple Treatment Models under Discrete Variation," Papers 2307.06174, arXiv.org.
    • Handle: RePEc:arx:papers:2307.06174
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    References listed on IDEAS

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