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Robust and Efficient Estimation of Potential Outcome Means under Random Assignment

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  • Akanksha Negi
  • Jeffrey M. Wooldridge

Abstract

We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply these regression adjustment techniques to efficiently estimate the lower bound mean willingness to pay for an oil spill prevention program in California.

Suggested Citation

  • Akanksha Negi & Jeffrey M. Wooldridge, 2020. "Robust and Efficient Estimation of Potential Outcome Means under Random Assignment," Papers 2010.01800, arXiv.org, revised Aug 2024.
    • Handle: RePEc:arx:papers:2010.01800
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    References listed on IDEAS

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    1. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    2. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    3. Masahide Watanabe, 2010. "Nonparametric Estimation of Mean Willingness to Pay from Discrete Response Valuation Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(4), pages 1114-1135.
    4. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    5. Joshua Angrist & Eric Bettinger & Michael Kremer, 2006. "Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia," American Economic Review, American Economic Association, vol. 96(3), pages 847-862, June.
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    Cited by:

    1. John A. List & Ian Muir & Gregory Sun, 2024. "Using machine learning for efficient flexible regression adjustment in economic experiments," Econometric Reviews, Taylor & Francis Journals, vol. 44(1), pages 2-40, July.

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