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Causal Inference and Impact Evaluation

Author

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  • Denis Fougère

    (CNRS - Centre National de la Recherche Scientifique, OSC - Observatoire sociologique du changement (Sciences Po, CNRS) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, LIEPP - Laboratoire interdisciplinaire d'évaluation des politiques publiques (Sciences Po) - Sciences Po - Sciences Po, CEPR - Center for Economic Policy Research - CEPR, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics)

  • Nicolas Jacquemet

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

This paper describes, in a non-technical way, the main impact evaluation methods, both experimental and quasi-experimental, and the statistical model underlying them. In the first part, we provide a brief survey of the papers making use of those methods that have been published by the journal Economie et Statistique / Economics and Statistics over the past fifteen years. In the second part, some of the most important methodological advances to have recently been put forward in this field of research are presented. To finish, we focus not only on the need to pay particular attention to the accuracy of the estimated effects, but also on the requirement to replicate evaluations, carried out by experimentation or quasi-experimentation, in order to distinguish false positives from proven effects.

Suggested Citation

  • Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," SciencePo Working papers Main hal-02866828, HAL.
  • Handle: RePEc:hal:spmain:hal-02866828
    DOI: 10.24187/ecostat.2019.510t.1996
    Note: View the original document on HAL open archive server: https://hal.science/hal-02866828
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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. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71, Elsevier.
    3. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    4. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    5. Véronique Simonnet & Elisabeth Danzin, 2014. "L'effet du RSA sur le taux de retour à l'emploi des allocataires. Une analyse en double différence selon le nombre et l'âge des enfants," Économie et Statistique, Programme National Persée, vol. 467(1), pages 91-116.
    6. Francesco Avvisati & Marc Gurgand & Nina Guyon & Eric Maurin, 2014. "Getting Parents Involved: A Field Experiment in Deprived Schools," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(1), pages 57-83.
    7. Steven Glazerman & Dan M. Levy & David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," The ANNALS of the American Academy of Political and Social Science, , vol. 589(1), pages 63-93, September.
    8. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 531-580.
    9. Michael A. Clemens, 2017. "The Meaning Of Failed Replications: A Review And Proposal," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 326-342, February.
    10. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    11. Magali Beffy & Denis Fougère & Arnaud Maurel, 2009. "L’impact du travail salarié des étudiants sur la réussite et la poursuite des études universitaires," Économie et Statistique, Programme National Persée, vol. 422(1), pages 31-50.
    12. List, John A, et al, 2001. "Academic Economists Behaving Badly? A Survey on Three Areas of Unethical Behavior," Economic Inquiry, Western Economic Association International, vol. 39(1), pages 162-170, January.
    13. Luc Behaghel & Bruno Crépon & Béatrice Sédillot, 2004. "Contribution Delalande et transitions sur le marché du travail," Économie et Statistique, Programme National Persée, vol. 372(1), pages 61-88.
    14. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    15. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    16. Jacquemet,Nicolas & L'Haridon,Olivier, 2018. "Experimental Economics," Cambridge Books, Cambridge University Press, number 9781107060272.
    17. Yingying Dong & Arthur Lewbel, 2015. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1081-1092, December.
    18. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    19. 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.
    20. Rachael Meager, 2019. "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 57-91, January.
    21. Bruno Crépon & Florencia Devoto & Esther Duflo & William Parienté, 2015. "Estimating the Impact of Microcredit on Those Who Take It Up: Evidence from a Randomized Experiment in Morocco," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 123-150, January.
    22. 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.
    23. Duncan D. Chaplin & Thomas D. Cook & Jelena Zurovac & Jared S. Coopersmith & Mariel M. Finucane & Lauren N. Vollmer & Rebecca E. Morris, 2018. "The Internal And External Validity Of The Regression Discontinuity Design: A Meta‐Analysis Of 15 Within‐Study Comparisons," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 37(2), pages 403-429, March.
    24. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    25. James Bisbee & Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2017. "Local Instruments, Global Extrapolation: External Validity of the Labor Supply-Fertility Local Average Treatment Effect," Journal of Labor Economics, University of Chicago Press, vol. 35(S1), pages 99-147.
    26. Ghislain Geniaux & Claude Napoléone, 2011. "Évaluation des effets des zonages environnementaux sur la croissance urbaine et l’activité agricole," Économie et Statistique, Programme National Persée, vol. 444(1), pages 181-199.
    27. Deirdre N. McCloskey & Stephen T. Ziliak, 1996. "The Standard Error of Regressions," Journal of Economic Literature, American Economic Association, vol. 34(1), pages 97-114, March.
    28. Guillaume Bérard & Alain Trannoy, 2018. "The impact of the 2014 increase in the real estate transfer taxes on the French housing market," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 500-501-5, pages 179-200.
    29. Fougère, Denis & Barone, Carlo & Pin, Clément, 2019. "Social origins, shared book reading and language skills in early childhood: evidence from an information experiment," CEPR Discussion Papers 14006, C.E.P.R. Discussion Papers.
    30. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70, Elsevier.
    31. Lan Liu & Michael G. Hudgens, 2014. "Large Sample Randomization Inference of Causal Effects in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 288-301, March.
    32. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
    33. Jinyong Hahn & Ruoyao Shi, 2017. "Synthetic Control and Inference," Econometrics, MDPI, vol. 5(4), pages 1-12, November.
    34. Pascale Petit & Emmanuel Duguet & Yannick L’Horty & Loïc du Parquet & Florent Sari, 2013. "Discrimination à l'embauche : les effets du genre et de l'origine se cumulent-ils systématiquement ?," Économie et Statistique, Programme National Persée, vol. 464(1), pages 141-153.
    35. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    More about this item

    Keywords

    Causal effects; Causal inference; Evaluation methods;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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