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Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable

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  • Aronow, Peter M.
  • Carnegie, Allison

Abstract

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

Suggested Citation

  • Aronow, Peter M. & Carnegie, Allison, 2013. "Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable," Political Analysis, Cambridge University Press, vol. 21(4), pages 492-506.
    • Handle: RePEc:cup:polals:v:21:y:2013:i:04:p:492-506_01
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    Citations

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    Cited by:

    1. Wunsch, Conny & Strobl, Renate, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
    2. Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2021. "From Local to Global: External Validity in a Fertility Natural Experiment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 217-243, January.
    3. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    4. Hans Fricke & Markus Frölich & Martin Huber & Michael Lechner, 2020. "Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 481-504, August.
    5. Kumar, Anil & Liang, Che-Yuan, 2020. "Estimating taxable income responses with elasticity heterogeneity," Journal of Public Economics, Elsevier, vol. 188(C).
    6. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Instrumental variable estimation of the causal hazard ratio," Biometrics, The International Biometric Society, vol. 79(2), pages 539-550, June.
    7. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    8. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    9. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    10. Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
    11. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.
    12. Pengfei Wang & Xiang Wei & Diancheng Hu & Fang Meng, 2022. "Does Leisure Contribute to the Improvement of Individual Job Performance? A Field Tracking Study Based on the Chinese Manufacturing Industry," Sustainability, MDPI, vol. 14(11), pages 1-17, May.

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