Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments

John List, Ian Muir and Gregory Sun

Natural Field Experiments from The Field Experiments Website

Abstract: 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.

Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-exp
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Using machine learning for efficient flexible regression adjustment in economic experiments (2024)
Working Paper: Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:feb:natura:00763

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