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Revisiting regression adjustment in experiments with heterogeneous treatment effects

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  • Akanksha Negi
  • Jeffrey M. Wooldridge

Abstract

In the context of random sampling, we show that linear full (separate) regression adjustment (FRA) on the control and treatment groups is, asymptotically, no less efficient than both the simple difference-in-means estimator and the pooled regression adjustment estimator; with heterogeneous treatment effects, FRA is usually strictly more efficient. We also propose a class of nonlinear regression adjustment estimators where consistency is ensured despite arbitrary misspecification of the conditional mean function. A simulation study confirms that nontrivial efficiency gains are possible with linear FRA, and that further gains are possible, even under severe mean misspecification, using nonlinear FRA.

Suggested Citation

  • Akanksha Negi & Jeffrey M. Wooldridge, 2021. "Revisiting regression adjustment in experiments with heterogeneous treatment effects," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 504-534, April.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:5:p:504-534
    DOI: 10.1080/07474938.2020.1824732
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    Citations

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    Cited by:

    1. Joao M.C. Santos Silva & Rainer Winkelmann, 2024. "MisspecifiÂ…ed Exponential Regressions: Estimation, Interpretation, and Average Marginal Effects," School of Economics Discussion Papers 0124, School of Economics, University of Surrey.
    2. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    3. 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.
    4. Sloczynski, Tymon, 2020. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," IZA Discussion Papers 13283, Institute of Labor Economics (IZA).
    5. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.
    6. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org.
    7. Andrews Doeh Agblobi & Anthony Kofi Osei-Fosu & Hadrat Yusif, 2020. "Poverty Response to the Household Type of Elderly and Old-Age Pension," Business and Management Research, Business and Management Research, Sciedu Press, vol. 9(4), pages 1-20, December.
    8. Siddik, Abu Bakkar & Khan, Samiha & Khan, Uzma & Yong, Li & Murshed, Muntasir, 2023. "The role of renewable energy finance in achieving low-carbon growth: contextual evidence from leading renewable energy-investing countries," Energy, Elsevier, vol. 270(C).
    9. Murshed, Muntasir & Apergis, Nicholas & Alam, Md Shabbir & Khan, Uzma & Mahmud, Sakib, 2022. "The impacts of renewable energy, financial inclusivity, globalization, economic growth, and urbanization on carbon productivity: Evidence from net moderation and mediation effects of energy efficiency," Renewable Energy, Elsevier, vol. 196(C), pages 824-838.
    10. Max Cytrynbaum, 2023. "Covariate Adjustment in Stratified Experiments," Papers 2302.03687, arXiv.org, revised Sep 2023.
    11. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.
    12. Sebastian Schongen, 2023. "Digitalisation as a Prospect for Work–Life Balance and Inclusion: A Natural Experiment in German Hospitals," Social Inclusion, Cogitatio Press, vol. 11(4), pages 225-238.
    13. Yuehao Bai & Liang Jiang & Joseph P. Romano & Azeem M. Shaikh & Yichong Zhang, 2023. "Covariate Adjustment in Experiments with Matched Pairs," Papers 2302.04380, arXiv.org, revised Oct 2023.

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