Overlooked biases from misidentifications of causal structures
File(s)
Published version
Author(s)
Cenci, Simone
Type
Journal Article
Abstract
Testing theories and explaining phenomena in empirical finance often requires estimating causal effects from observational data. In this note, we argue that some of the standard practices to address endogeneity concerns in regression-based estimation approaches can, when not correctly implemented and their results not appropriately interpreted, generate additional, often overlooked, problems. We identify three main systemic issues in empirical finance, provide theoretical and numerical examples to illustrate and support our arguments, and propose solutions to overcome these limitations. Overall, we suggest that these issues are caused by a systematic underestimation of the importance of robust ex-ante identification, and interpretation, of causal structures in empirical studies in finance.
Date Issued
2024-12
Date Acceptance
2024-02-21
Citation
The Journal of Finance and Data Science, 2024, 10, pp.1-8
ISSN
2405-9188
Publisher
Elsevier
Start Page
1
End Page
8
Journal / Book Title
The Journal of Finance and Data Science
Volume
10
Copyright Statement
© 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under
the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
https://www.sciencedirect.com/science/article/pii/S2405918824000126?via%3Dihub
Publication Status
Published
Article Number
100127
Date Publish Online
2024-02-29