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2SLS with multiple treatments

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  • Bhuller, Manudeep
  • Sigstad, Henrik

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.

Suggested Citation

  • Bhuller, Manudeep & Sigstad, Henrik, 2024. "2SLS with multiple treatments," Journal of Econometrics, Elsevier, vol. 242(1).
    • Handle: RePEc:eee:econom:v:242:y:2024:i:1:s0304407624001313
    DOI: 10.1016/j.jeconom.2024.105785
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    1. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-Based Identification with Formula Instruments: A Review," NBER Working Papers 31393, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Multiple treatments; Monotonicity; Instrumental variables; Two-stage least squares;
    All these keywords.

    JEL classification:

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

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