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Interpretational errors with instrumental variables

Author

Listed:
  • Luca Locher
  • Mats J. Stensrud
  • Aaron L. Sarvet

Abstract

Instrumental variables (IV) are often used to identify causal effects in observational settings and experiments subject to non-compliance. Under canonical assumptions, IVs allow us to identify a so-called local average treatment effect (LATE). The use of IVs is often accompanied by a pragmatic decision to abandon the identification of the causal parameter that corresponds to the original research question and target the LATE instead. This pragmatic decision presents a potential source of error: an investigator mistakenly interprets findings as if they had made inference on their original causal parameter of interest. We conducted a systematic review and meta-analysis of patterns of pragmatism and interpretational errors in the applied IV literature published in leading journals of economics, political science, epidemiology, and clinical medicine (n = 309 unique studies). We found that a large fraction of studies targeted the LATE, although specific interest in this parameter was rare. Of these studies, 61% contained claims that mistakenly suggested that another parameter was targeted -- one whose value likely differs, and could even have the opposite sign, from the parameter actually estimated. Our findings suggest that the validity of conclusions drawn from IV applications is often compromised by interpretational errors.

Suggested Citation

  • Luca Locher & Mats J. Stensrud & Aaron L. Sarvet, 2025. "Interpretational errors with instrumental variables," Papers 2509.02045, arXiv.org.
  • Handle: RePEc:arx:papers:2509.02045
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    References listed on IDEAS

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    1. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    3. Christine Blandhol & John Bonney & Magne Mogstad & Alexander Torgovitsky, 2022. "When is TSLS Actually LATE?," NBER Working Papers 29709, National Bureau of Economic Research, Inc.
    4. Guilherme Duarte & Noam Finkelstein & Dean Knox & Jonathan Mummolo & Ilya Shpitser, 2024. "An Automated Approach to Causal Inference in Discrete Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(547), pages 1778-1793, July.
    5. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    6. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6a.
    7. Ufuk Akcigit & John Grigsby & Tom Nicholas & Stefanie Stantcheva, 2022. "Taxation and Innovation in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 329-385.
    8. Jason Abaluck & Mauricio Caceres Bravo & Peter Hull: & Amanda Starc, 2021. "Mortality Effects and Choice Across Private Health Insurance Plans," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1557-1610.
    9. J.J. Heckman & E.E. Leamer (ed.), 2007. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 6, number 6b.
    10. Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021. "Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2799-2832.
    11. Kyle Greenberg & Matthew Gudgeon & Adam Isen & Corbin Miller & Richard Patterson, 2022. "Army Service in the All-Volunteer Era," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 2363-2418.
    12. Lisa D Cook & Maggie E C Jones & Trevon D Logan & David Rosé, 2023. "The Evolution of Access to Public Accommodations in the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 37-102.
    13. Olivier Coibion & Yuriy Gorodnichenko & Tiziano Ropele, 2020. "Inflation Expectations and Firm Decisions: New Causal Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 165-219.
    14. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    15. repec:fth:prinin:455 is not listed on IDEAS
    16. Marbach, Moritz & Hangartner, Dominik, 2020. "Profiling Compliers and Noncompliers for Instrumental-Variable Analysis," Political Analysis, Cambridge University Press, vol. 28(3), pages 435-444, July.
    17. Bruno Caprettini & Hans-Joachim Voth, 2023. "New Deal, New Patriots: How 1930s Government Spending Boosted Patriotism During World War II," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 465-513.
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