Multiple testing with covariate adjustment in experimental economics

Summary This paper provides a framework for testing multiple null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. Using general results from the multiple testing literature, we develop under weak assumptions a procedure t...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2023-09, Vol.38 (6), p.920-939
Main Authors: List, John A., Shaikh, Azeem M., Vayalinkal, Atom
Format: Article
Language:English
Subjects:
Asymptotic properties
Econometrics
Experimental economics
experiments
familywise error rate
Hypotheses
multiple testing
Null hypothesis
Power
Probability
Random sampling
Statistical tests
Tests
treatment effects
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Summary:Summary This paper provides a framework for testing multiple null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. Using general results from the multiple testing literature, we develop under weak assumptions a procedure that (i) asymptotically controls the familywise error rate—the probability of one or more false rejections—and (ii) is asymptotically balanced in that the marginal probability of rejecting any true null hypothesis is approximately equal in large samples. Our procedure improves upon classical methods by incorporating information about the joint dependence structure of the test statistics when determining which null hypotheses to reject, leading to gains in power. An important point of departure from prior work is that we exploit observed, baseline covariates to obtain further gains in power. The precise way in which we incorporate these covariates is based on recent results from the statistics literature in order to ensure that inferences are typically more powerful in large samples.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2985