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2SLS with Multiple Treatments

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  • Manudeep Bhuller
  • Henrik Sigstad

Abstract

We study what two-stage least squares (2SLS) identifies in models with multiple treatments under treatment effect heterogeneity. Two conditions are shown to be necessary and sufficient for the 2SLS to identify positively weighted sums of agent-specific effects of each treatment: average conditional monotonicity and no cross effects. Our identification analysis allows for any number of treatments, any number of continuous or discrete instruments, and the inclusion of covariates. We provide testable implications and present characterizations of choice behavior implied by our identification conditions.

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  • Manudeep Bhuller & Henrik Sigstad, 2022. "2SLS with Multiple Treatments," Papers 2205.07836, arXiv.org, revised May 2024.
    • Handle: RePEc:arx:papers:2205.07836
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    1. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-based identification with formula instruments: A review," CeMMAP working papers 12/23, Institute for Fiscal Studies.

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    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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