August 18, 2025

When will causal structure learning become practical?

Key Points

Causal structure learning enhances our ability to identify relationships between variables, transforming data analysis practices.
This approach may significantly inform medical and epidemiological research, potentially yielding new insights.
Using advanced algorithms, researchers explore data patterns to uncover underlying causal structures.
Wider adoption of causal structure learning could lead to breakthroughs in public health strategies and policies.

Abstract

Journal Article When will causal structure learning become practical? Get access Johannes Textor Johannes Textor Institute for Computing and Information Sciences, Radboud University, Nijmegen, The NetherlandsMedical BioSciences, Radboudumc, Nijmegen, The Netherlands Corresponding author. Institute for Computing and Information Sciences, Radboud University, Toernooiveld 212, 6525 EC Nijmegen, The Netherlands. E-mail: email protected https://orcid.org/0000-0002-0459-9458 Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 54, Issue 5, October 2025, dyaf145, https://doi.org/10.1093/ije/dyaf145 Published: 22 August 2025 Article history Received: 04 December 2024 Editorial decision: 22 July 2025 Accepted: 31 July 2025 Published: 22 August 2025

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