Model-X knockoffs in the replication crisis era: Reducing false discoveries and researcher bias in social science research

Zhou, Jing and Scherr, Sebastian (2025) Model-X knockoffs in the replication crisis era: Reducing false discoveries and researcher bias in social science research. Social Sciences & Humanities Open, 11. ISSN 2590-2911

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Abstract

The present study addresses problems faced by data-driven social science caused by having too much or not enough data. In particular, an abundance of data or a (sudden) lack thereof makes it challenging to identify the most important predictors in a sea of noise using the most parsimonious and reproducible model possible. In this article, we present the model-X knockoff method, which was introduced by Candès et al. (2018) for reducing the false identification of significant effects due to flexibility-ambiguity issues, to a broader audience, particularly within the social sciences and humanities. Our goal is to provide an accessible starting point and ideally spark interest among researchers in these fields to explore how model-X knockoffs can enhance their work. The findings from a performance contrast simulation indicate that model-X knockoffs select fewer relevant variables than other statistical methods to automatically identify variables, resulting in fewer mistakes. The simulation findings also demonstrate that model-X knockoffs are stable and less sensitive to even small changes in the dataset than other procedures, making them a viable way to reduce researcher degrees of freedom and increase the reproducibility of scientific findings. An additional real data example demonstrates the operational utility of the simulation.

Item Type:	Article
Uncontrolled Keywords:	big data,high dimensional statistics,model-x knockoffs,multivariate linear regression,simulation study,true model,variable selection,decision sciences (miscellaneous),psychology (miscellaneous),social sciences (miscellaneous) ,/dk/atira/pure/subjectarea/asjc/1800/1801
Faculty \ School:	Faculty of Science > School of Engineering, Mathematics and Physics
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	04 Mar 2025 17:30
Last Modified:	10 Mar 2025 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98672
DOI:	10.1016/j.ssaho.2025.101380

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