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Policy evaluation with multiple instrumental variables

Author

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  • Mogstad, Magne
  • Torgovitsky, Alexander
  • Walters, Christopher R.
Abstract
Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. This condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. We develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity condition is implied by standard choice theory and allows for rich unobserved heterogeneity even in the presence of multiple instruments. The new framework can be viewed as having multiple different choice models for the same observed treatment variable, all of which must be consistent with the data and with each other. Using this framework, we develop a methodology for partial identification of clearly stated, policy-relevant target parameters while allowing for a wide variety of nonparametric shape restrictions and parametric functional form assumptions. We show how the methodology can be used to combine multiple instruments together to yield more informative empirical conclusions than one would obtain by using each instrument separately. The methodology provides a blueprint for extracting and aggregating information from multiple controlled or natural experiments while still allowing for rich unobserved heterogeneity in both treatment effects and choice behavior.

Suggested Citation

  • Mogstad, Magne & Torgovitsky, Alexander & Walters, Christopher R., 2024. "Policy evaluation with multiple instrumental variables," Journal of Econometrics, Elsevier, vol. 243(1).
    • Handle: RePEc:eee:econom:v:243:y:2024:i:1:s0304407624000642
    DOI: 10.1016/j.jeconom.2024.105718
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    Citations

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    Cited by:

    1. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.
    2. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    3. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Leonard Goff, 2024. "When does IV identification not restrict outcomes?," Papers 2406.02835, arXiv.org, revised Sep 2024.
    5. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2024.
    6. Bhuller, Manudeep & Sigstad, Henrik, 2024. "2SLS with multiple treatments," Journal of Econometrics, Elsevier, vol. 242(1).
    7. Shengli Dai & Weimin Zhang & Linshan Lan, 2022. "Quantitative Evaluation of China’s Ecological Protection Compensation Policy Based on PMC Index Model," IJERPH, MDPI, vol. 19(16), pages 1-24, August.
    8. Stephen Coussens & Jann Spiess, 2021. "Improving Inference from Simple Instruments through Compliance Estimation," Papers 2108.03726, arXiv.org.
    9. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
    10. Vishal Kamat & Samuel Norris & Matthew Pecenco, 2023. "Identification in Multiple Treatment Models under Discrete Variation," Papers 2307.06174, arXiv.org.
    11. van ’t Hoff, Nadja & Lewbel, Arthur & Mellace, Giovanni, 2023. "Limited Monotonicity and the Combined Compliers LATE," Discussion Papers on Economics 2/2023, University of Southern Denmark, Department of Economics.
    12. Mustafa Özer & Jan Fidrmuc, 2024. "Does Immigration Affect the Natives’ Mental Health? Causal Evidence from Forced Syrian Migration to Turkey," CESifo Working Paper Series 11399, CESifo.
    13. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Oct 2023.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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