Integrating Background Knowledge for Scalable Causal Discovery
2026-07-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explain that when trying to figure out cause-and-effect relationships, having expert knowledge about the system can be very helpful. Usually, this knowledge is only used after building the cause-effect map, but the authors suggest using it during the process to make it faster and more accurate. They created a method to do this and tested it with different algorithms, finding that it made the discovery easier and improved results. Their work is especially useful when dealing with many variables, where computations can get very complicated.
causal discoverycausal graphbackground knowledgeidentifiabilitycomputational scalabilitypostprocessingcausal effectsgraph structurealgorithmscausal inference
Authors
Mátyás Schubert, Theofanis Aslanidis, Tom Claassen, Sara Magliacane
Abstract
Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure, but also in reducing the space of candidate causal graphs. As causal discovery can become computationally expensive for large number of variables, it is crucial to utilize background knowledge effectively during the causal discovery process. However, most current methods only use background knowledge in a postprocessing step after causal discovery to refine the learned graph. In this work, we develop a framework for utilizing background knowledge during the causal discovery process, focusing especially on scalable causal discovery methods that recover only a subset of the whole graph. We implement our framework for multiple algorithms and empirically show that utilizing background knowledge can both reduce computational requirements and increase the quality of the learned structures.