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Weighting for External Validity

Author

Listed:
  • Isaiah Andrews
  • Emily Oster

Abstract

External validity is a challenge in treatment effect estimation. Even in randomized trials, the experimental sample often differs from the population of interest. If participation decisions are explained by observed variables such differences can be overcome by reweighting. However, participation may depend on unobserved variables. Even in such cases, under a common support assumption there exist weights which, if known, would allow reweighting the sample to match the population. While these weights cannot in general be estimated, we develop approximations which relate them to the role of private information in participation decisions. These approximations suggest benchmarks for assessing external validity.

Suggested Citation

  • Isaiah Andrews & Emily Oster, 2017. "Weighting for External Validity," NBER Working Papers 23826, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23826
    Note: DEV HE LS TWP
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    References listed on IDEAS

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    1. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2015. "Inference under covariate-adaptive randomization," CeMMAP working papers CWP45/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. 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.
    3. Marinho Bertanha & Guido W. Imbens, 2014. "External Validity in Fuzzy Regression Discontinuity Designs," NBER Working Papers 20773, National Bureau of Economic Research, Inc.
    4. Dehejia, Rajeev & Pop-Eleches, Cristian & Samii, Cyrus, 2015. "From Local to Global: External Validity in a Fertility Natural Experiment," IZA Discussion Papers 9300, Institute of Labor Economics (IZA).
    5. 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.
    6. repec:aea:aecrev:v:108:y:2018:i:10:p:3028-56 is not listed on IDEAS
    7. repec:ucp:jpolec:doi:10.1086/692712 is not listed on IDEAS
    8. 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.
    9. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, July - De.
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    Citations

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    Cited by:

    1. Andrew Dustan & Stanislao Maldonado & Juan Manuel Hernandez-Agramonte, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale field experiments in Peru," Working Papers 136, Peruvian Economic Association.
    2. 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.
    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).

    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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