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Treatment Effect Heterogeneity

Author

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  • Smith, Jeffrey A.

    (University of Wisconsin-Madison)

Abstract

Knowledge of treatment effect heterogeneity or "essential heterogeneity" plays an important role in our understanding of how programs work and in the design of systems to allocate them among the eligible. This paper provides a relatively non-technical survey of the current state of the treatment effect heterogeneity enterprise within economics from both substantive and applied econometric perspectives. It also suggests directions for research on treatment effect heterogeneity going forward.

Suggested Citation

  • Smith, Jeffrey A., 2022. "Treatment Effect Heterogeneity," IZA Discussion Papers 15151, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15151
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    References listed on IDEAS

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    1. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    2. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    3. Cunha, Flavio & Heckman, James J., 2007. "Identifying and Estimating the Distributions of Ex Post and Ex Ante Returns to Schooling," Labour Economics, Elsevier, vol. 14(6), pages 870-893, December.
    4. Angus Deaton, 2010. "Understanding the Mechanisms of Economic Development," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 3-16, Summer.
    5. Michael Lechner & Stephan Wiehler, 2011. "Kids or courses? Gender differences in the effects of active labor market policies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 24(3), pages 783-812, July.
    6. Lise, Jeremy & Seitz, Shannon & Smith, Jeffrey A., 2003. "Equilibrium Policy Experiments and the Evaluation of Social Programs," IZA Discussion Papers 758, Institute of Labor Economics (IZA).
    7. Lechner, Michael & Smith, Jeffrey, 2007. "What is the value added by caseworkers?," Labour Economics, Elsevier, vol. 14(2), pages 135-151, April.
    8. David McKenzie, 2018. "Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 714-722, October.
    9. Robert J. LaLonde, 2003. "Employment and Training Programs," NBER Chapters, in: Means-Tested Transfer Programs in the United States, pages 517-586, National Bureau of Economic Research, Inc.
    10. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 101-136.
    11. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    12. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    13. Michael J. Weiss & Howard S. Bloom & Thomas Brock, 2014. "A Conceptual Framework For Studying The Sources Of Variation In Program Effects," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 33(3), pages 778-808, June.
    14. Dan A. Black & Jeffrey A. Smith & Mark C. Berger & Brett J. Noel, 2003. "Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System," American Economic Review, American Economic Association, vol. 93(4), pages 1313-1327, September.
    15. Patrick Kline & Melissa Tartari, 2016. "Bounding the Labor Supply Responses to a Randomized Welfare Experiment: A Revealed Preference Approach," American Economic Review, American Economic Association, vol. 106(4), pages 972-1014, April.
    16. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 651-694.
    17. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    18. Sandner, Malte & Cornelissen, Thomas & Jungmann, Tanja & Herrmann, Peggy, 2018. "Evaluating the effects of a targeted home visiting program on maternal and child health outcomes," Journal of Health Economics, Elsevier, vol. 58(C), pages 269-283.
    19. Jeffrey Smith & Alexander Whalley & Nathaniel Wilcox, 2021. "Are Participants Good Evaluators?," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number apge.
    20. Mark M. Pitt & Mark R. Rosenzweig & Mohammad Nazmul Hassan, 2012. "Human Capital Investment and the Gender Division of Labor in a Brawn-Based Economy," American Economic Review, American Economic Association, vol. 102(7), pages 3531-3560, December.
    21. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    22. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    23. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
    24. 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.
    25. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
    26. 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.
    27. Wiswall, Matthew, 2013. "The dynamics of teacher quality," Journal of Public Economics, Elsevier, vol. 100(C), pages 61-78.
    28. Anthony Bald & Eric Chyn & Justine Hastings & Margarita Machelett, 2022. "The Causal Impact of Removing Children from Abusive and Neglectful Homes," Journal of Political Economy, University of Chicago Press, vol. 130(7), pages 1919-1962.
    29. repec:hal:pseose:halshs-00840901 is not listed on IDEAS
    30. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    31. repec:pri:rpdevs:deaton_understanding_mechanisms_of_economic_development_with_abstract_apr is not listed on IDEAS
    32. repec:mpr:mprres:8058 is not listed on IDEAS
    33. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    34. Seán M. Muller, 2015. "Causal Interaction and External Validity: Obstacles to the Policy Relevance of Randomized Evaluations," The World Bank Economic Review, World Bank, vol. 29(suppl_1), pages 217-225.
    35. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2017. "Can Variation in Subgroups' Average Treatment Effects Explain Treatment Effect Heterogeneity? Evidence from a Social Experiment," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 683-697, July.
    36. Bell, Stephen H. & Orr, Larry L., 2002. "Screening (and creaming?) applicants to job training programs: the AFDC homemaker-home health aide demonstrations," Labour Economics, Elsevier, vol. 9(2), pages 279-301, April.
    37. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    38. Rothschild, Michael & White, Lawrence J, 1995. "The Analytics of the Pricing of Higher Education and Other Services in Which the Customers Are Inputs," Journal of Political Economy, University of Chicago Press, vol. 103(3), pages 573-586, June.
    39. Peng Ding & Avi Feller & Luke Miratrix, 2019. "Decomposing Treatment Effect Variation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 304-317, January.
    40. Smith, Jeffrey A. & Whalley, Alexander & Wilcox, Nathaniel T., 2020. "Are Program Participants Good Evaluators?," IZA Discussion Papers 13584, Institute of Labor Economics (IZA).
    41. Eric A. Hanushek & Steven G. Rivkin, 2012. "The Distribution of Teacher Quality and Implications for Policy," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 131-157, July.
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    Cited by:

    1. Brade, Raphael, 2022. "Social Information and Educational Investment - Nudging Remedial Math Course Participation," MPRA Paper 113076, University Library of Munich, Germany.

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    More about this item

    Keywords

    treatment effects; essential heterogeneity; program evaluation;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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