Call for a paradigm shift from statistical causal inference to multi-evidence causal investigation

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Explicit discussions of causal methods have long fallen into the domain of statistics. Scientists have instead pursued mechanistic knowledge as an alternative approach to causal understanding. In the past two decades, a body of literature has developed that constitutes a statistical causal inference paradigm based on restrictive assumptions that fail to respect mechanistic knowledge. Recent evaluations have shown this paradigm to be incomplete and insufficient, leading to a call for its replacement by an expanded multi-evidence paradigm capable of considering mechanistic evidence and building causal knowledge across studies. Methods for mechanistic causal inference have now been described and are illustrated here, making clear the strong case for scientists to adopt a multi-evidence paradigm. https://doi.org/10.32942/X2G95Z Life Sciences causal methods, causal inference, mechanistic causal inference Published: 2026-03-06 20:15 Last Updated: 2026-03-06 20:15 CC-By Attribution-NonCommercial-NoDerivatives 4.0 International Conflict of interest statement: none Data and Code Availability Statement: Data used in plots contained in Fig. 1 available from Mendeley Data Repository (DOI: 10.17632/jwc4rr6kwr.3). Language: English