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A Measure of Robustness to Misspecification

Author

Listed:
  • Susan Athey
  • Guido Imbens

Abstract

Researchers often report estimates and standard errors for the object of interest (such as a treatment effect) based on a single specification of a statistical model. We propose a systematic approach to assessing sensitivity to specification. We construct estimates of the object of interest for each of a large set of models. Our proposed robustness measure is the standard deviation of the point estimates over the set of models. Each member of the set is generated by splitting the sample into two subsamples based on covariate values, constructing separate parameter estimates for each subsample, and then combining the results.

Suggested Citation

  • Susan Athey & Guido Imbens, 2015. "A Measure of Robustness to Misspecification," American Economic Review, American Economic Association, vol. 105(5), pages 476-480, May.
    • Handle: RePEc:aea:aecrev:v:105:y:2015:i:5:p:476-80
    Note: DOI: 10.1257/aer.p20151020
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Guido W. Imbens & Donald B. Rubin & Bruce I. Sacerdote, 2001. "Estimating the Effect of Unearned Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players," American Economic Review, American Economic Association, vol. 91(4), pages 778-794, September.
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    Cited by:

    1. Peter G. Backus & Nicky L. Grant, 2019. "How sensitive is the average taxpayer to changes in the tax-price of giving?," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(2), pages 317-356, April.
    2. Peter Backus & Maria Cubel & Matej Guid & Santiago Sánchez‐Pagés & Enrique López Mañas, 2023. "Gender, competition, and performance: Evidence from chess players," Quantitative Economics, Econometric Society, vol. 14(1), pages 349-380, January.
    3. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    4. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    5. Peter Backus & María Cubel & Matej Guid & Santiago Sánchez-Pages & Enrique Lopez Manas, 2016. "Gender, competition and performance:Evidence from real tournaments," Working Papers 2016/27, Institut d'Economia de Barcelona (IEB).
    6. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    7. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," Ruhr Economic Papers 1055, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Thorsten Sellhorn, 2020. "Machine Learning und empirische Rechnungslegungsforschung: Einige Erkenntnisse und offene Fragen [Machine Learning and Empirical Accounting Research: Some Findings and Open Questions]," Schmalenbach Journal of Business Research, Springer, vol. 72(1), pages 49-69, March.
    9. Jason M. Rathje & Riitta Katila, 2021. "Enabling Technologies and the Role of Private Firms: A Machine Learning Matching Analysis," Strategy Science, INFORMS, vol. 6(1), pages 5-21, March.
    10. Furno, Marilena, 2021. "The synthetic control approach: Multivalued treatments at the quantiles," Research in Economics, Elsevier, vol. 75(1), pages 7-20.
    11. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    12. Mullally, Conner & Chakravarty, Shourish, 2018. "Are matching funds for smallholder irrigation money well spent?," Food Policy, Elsevier, vol. 76(C), pages 70-80.
    13. Zhang, Chi & Managi, Shunsuke, 2020. "Functional social support and maternal stress: A study on the 2017 paid parental leave reform in Japan," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 153-172.
    14. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
    15. Guido Imbens & Yiqing Xu, 2024. "LaLonde (1986) after Nearly Four Decades: Lessons Learned," Papers 2406.00827, arXiv.org, revised Jun 2024.
    16. Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.
    17. Masahiro Kato & Hikaru Kawarazaki, 2019. "Model Specification Test with Unlabeled Data: Approach from Covariate Shift," Papers 1911.00688, arXiv.org, revised Feb 2020.
    18. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    19. Bremus, Franziska & Kliatskova, Tatsiana, 2020. "Legal harmonization, institutional quality, and countries’ external positions: A sectoral analysis," Journal of International Money and Finance, Elsevier, vol. 107(C).

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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