Harnessing the Power of Knowledge Graphs to Improve Causal Discovery

Abstract

Reconstructing the structure of causal graphical models from observational data is crucial for identifying causal mechanisms in scientific research. However, real-world noise and hidden factors can make it difficult to detect true underlying causal relationships. Current methods mainly rely on extensive expert analysis to correct wrongly identified connections, guiding structure learning toward more accurate causal interactions. This reliance on expert input demands significant manual effort and is risky due to potential erroneous judgments when handling complex causal interactions. To address these issues, this paper introduces a new, expert-free method to improve causal discovery. By utilizing the extensive resources of static knowledge bases across various fields, specifically knowledge graphs (KGs), we extract causal information related to the variables of interest and use these as prior constraints in the structure learning process. Unlike the detailed constraints provided by expert analysis, the information from KGs is more general, indicating the presence of certain paths without specifying exact connections or their lengths. We incorporate these constraints in a soft way to reduce potential noise in the KG-derived priors, ensuring that our method remains reliable. Moreover, we provide interfaces for various mainstream causal discovery methods to enhance the utility of our approach. For empirical validation, we apply our approach across multiple areas of causal discovery. The results show that our method effectively enhances data-based causal discovery and demonstrates its promising applications.