Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology | Scientific Reports

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reports volume 10, Article number: 2975 (2020) Cite this article 27k Accesses 38 Citations 15 Altmetric Metrics details Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to

Download PDF Subjects Alzheimer's disease Cognitive ageing Abstract Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships.

related reading

• Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology | Scientific Reportsnature.com
• Frederick Eberhardtits.caltech.edu
• Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkersgoodfire.ai
• AI for Alzheimer’sopenaifoundation.org
• Gap Mapgap-map.org
• Deep learning of causal structures in high dimensions under data limitations | Nature Machine Intelligencenature.com
• SASE Caltechsase.caltech.edu
• [2605.27567] Why LLMs Fail at Causal Discovery and How Interventional Agents Escapearxiv.org
• Bryon Aragam // University of Chicagobryonaragam.com
• CausaLab — Can LLM Agents Discover Causal Mechanisms by Experiment?dylanzsz.github.io
• 1 Introduction – <span style='font-weight: 700'>Causal Inference</span><br/> <i style='color: #00b7ff'>The Mixtape</i>mixtape.scunning.com
• Getting In To A Causal Flow - by Ken Acquah - Causal Flowscausalflows.substack.com