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A Simple Approximation for Evaluating External Validity Bias

Author

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  • Isaiah Andrews
  • Emily Oster

Abstract

We develop a simple approximation that relates the total external validity bias in randomized trials to (i) bias from selection on observables and (ii) a measure for the role of treatment effect heterogeneity in driving selection into the experimental sample.

Suggested Citation

  • Isaiah Andrews & Emily Oster, 2017. "A Simple Approximation for Evaluating External Validity Bias," NBER Working Papers 23826, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23826
    Note: DEV EH LS TWP
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    1. 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.
    2. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    3. Amanda Kowalski, 2016. "Doing more when you're running LATE: Applying marginal treatment effect methods to examine treatment effect heterogeneity in experiments," Artefactual Field Experiments 00560, The Field Experiments Website.
    4. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 1-14, February.
    5. Nicholas Bloom & James Liang & John Roberts & Zhichun Jenny Ying, 2015. "Does Working from Home Work? Evidence from a Chinese Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(1), pages 165-218.
    6. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2017. "Beyond LATE with a Discrete Instrument," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 985-1039.
    7. Marinho Bertanha & Guido W. Imbens, 2020. "External Validity in Fuzzy Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 593-612, July.
    8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    9. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    10. 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.
    11. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    12. Pascaline Dupas & Jonathan Robinson, 2013. "Why Don't the Poor Save More? Evidence from Health Savings Experiments," American Economic Review, American Economic Association, vol. 103(4), pages 1138-1171, June.
    13. Alexander Gelber & Adam Isen & Judd B. Kessler, 2016. "The Effects of Youth Employment: Evidence from New York City Lotteries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 423-460.
    14. Orazio Attanasio & Adriana Kugler & Costas Meghir, 2011. "Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 188-220, July.
    15. 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.
    16. Erin Hartman & Richard Grieve & Roland Ramsahai & Jasjeet S. Sekhon, 2015. "From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 757-778, June.
    17. 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.
    18. Eric Chyn, 2018. "Moved to Opportunity: The Long-Run Effects of Public Housing Demolition on Children," American Economic Review, American Economic Association, vol. 108(10), pages 3028-3056, October.
    19. Karthik Muralidharan & Venkatesh Sundararaman, 2011. "Teacher Performance Pay: Experimental Evidence from India," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 39-77.
    20. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    21. Joshua D. Angrist & Miikka Rokkanen, 2015. "Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1331-1344, December.
    22. Benjamin A. Olken & Junko Onishi & Susan Wong, 2014. "Should Aid Reward Performance? Evidence from a Field Experiment on Health and Education in Indonesia," American Economic Journal: Applied Economics, American Economic Association, vol. 6(4), pages 1-34, October.
    23. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
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    Cited by:

    1. Andrew Dustan & Juan Manuel Hernandez-Agramonte & Stanislao Maldonado, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale," Natural Field Experiments 00664, The Field Experiments Website.
    2. Dustan, Andrew & Maldonado, Stanislao & Hernandez-Agramonte, Juan Manuel, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale field experiments in Peru," MPRA Paper 90952, University Library of Munich, Germany.
    3. Gauri, Varun & Jamison, Julian C. & Mazar, Nina & Ozier, Owen, 2019. "Motivating Bureaucrats through Social Recognition: External Validity — A Tale of Two States," IZA Discussion Papers 12251, Institute of Labor Economics (IZA).

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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