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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 estimating a vector of potential outcome means using linear regression adjustment when there are more than two treatment levels. We show that using separate regression adjustments for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate regression adjustment improves over pooled regression adjustment except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We also characterize the class of nonlinear regression adjustment methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply this general potential outcomes framework to a contingent valuation study for estimating 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.
    • 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. 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.
    3. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    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 K. Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," NBER Working Papers 30756, National Bureau of Economic Research, Inc.

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