Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments

John List, Ian Muir and Gregory K. Sun

No 30756, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This study investigates the optimal use of covariates in reducing variance when analyzing experimental data. We show that finding the variance-minimizing strategy for making use of pre-treatment observables is equivalent to estimating the conditional expectation function of the outcome given all available pre-randomization observables. This is a pure prediction problem, which recent advances in machine learning (ML) are well-suited to tackling. Through a number of empirical examples, we show how ML-based regression adjustments can feasibly be implemented in practical settings. We compare our proposed estimator to other standard variance reduction techniques in the literature. Two important advantages of our ML-based regression adjustment estimator are that (i) they improve asymptotic efficiency relative to other alternatives, and (ii) they can be implemented automatically, with relatively little tuning from the researcher, which limits the scope for data-snooping.

JEL-codes: C9 C90 C91 C93 (search for similar items in EconPapers)
Date: 2022-12
New Economics Papers: this item is included in nep-big, nep-cmp 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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