Toward Automating Causal Discovery in Financial Markets and Beyond

Alik Sokolov, Fabrizzio Sabelli, Behzad Azadie Faraz, Wuding Li and Luis Seco

Chapter 14 in Transactions of ADIA Lab:Interdisciplinary Advances in Data and Computational Science, 2025, pp 359-392 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This paper introduces a novel machine learning (ML) framework for causal discovery based on recent advances in large language models (LLMs) and discusses the applications of these causal discovery techniques to investment management. Unlike typical data-driven methods for data discovery, the framework using the implicit “world knowledge” in state-of-the-art LLMs to automate the expert judgment approach to causal discovery. A key application that is explored in detail is end-to-end causal factor analysis, where the authors demonstrate the utility of our method in specifying and analyzing detailed causal models for financial markets. This paper also conducts a comparative analysis, juxtaposing the new approach with conventional methods, to underscore the enhanced capability of the framework in revealing intricate causal dynamics in financial data.

Keywords: Computational Science; Data Science; AI Applications; Climate Science; Medical Imaging; Sustainability; Interdisciplinary Research; Data Science; Mathematical and Quantitative Finance (search for similar items in EconPapers)
JEL-codes: C45 C63 G11 Q54 (search for similar items in EconPapers)
Date: 2025
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