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Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments


  • John List
  • Ian Muir
  • Gregory Sun


This study investigates how to use regression adjustment to reduce variance in experimental data. We show that the estimators recommended in the literature satisfy an orthogonality property with respect to the parameters of the adjustment. This observation greatly simplifies the derivation of the asymptotic variance of these estimators and allows us to solve for the efficient regression adjustment in a large class of adjustments. Our efficiency results generalize a number of previous results known in the literature. We then discuss how this efficient regression adjustment can be feasibly implemented. We show the practical relevance of our theory in two ways. First, we use our efficiency results to improve common practices currently employed in field experiments. Second, we show how our theory allows researchers to robustly incorporate machine learning techniques into their experimental estimators to minimize variance.

Suggested Citation

  • John List & Ian Muir & Gregory Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," Natural Field Experiments 00763, The Field Experiments Website.
  • Handle: RePEc:feb:natura:00763

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    References listed on IDEAS

    1. Stefano DellaVigna & John A. List & Ulrike Malmendier, 2012. "Testing for Altruism and Social Pressure in Charitable Giving," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(1), pages 1-56.
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    4. Roland G. Fryer Jr & Steven D. Levitt & John A. List & Anya Samek, 2020. "Introducing CogX: A New Preschool Education Program Combining Parent and Child Interventions," NBER Working Papers 27913, National Bureau of Economic Research, Inc.
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    10. Greer K. Gosnell & John A. List & Robert D. Metcalfe, 2020. "The Impact of Management Practices on Employee Productivity: A Field Experiment with Airline Captains," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1195-1233.
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    12. Christopher Cotton & Brent R. Hickman & John List & Joseph P. Price & Sutanuka Roy, 2020. "Productivity Versus Motivation in Adolescent Human Capital Production: Evidence from a Structurally-Motivated Field Experiment," Working Paper 1444, Economics Department, Queen's University.
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    20. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099,, revised Jun 2013.
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    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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