Integrating Background Knowledge for Scalable Causal Discovery
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 60 references that are in the map. First posted on arXiv 11 Jul 2026.
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References · 28
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
- 2023Causal-learn: Causal Discovery in Pythoncited by 109
- 2023Causal Discovery with Language Models as Imperfect Expertscited by 64
- 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
- 2024Integrating Large Language Models in Causal Discovery: A Statistical Causal Approachcited by 32
- 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
- 2016Ancestral Causal Inferencecited by 24
- 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 23
- 2024On the Reliability of Large Language Models for Causal Discoverycited by 12
- 2022Valid Inference After Causal Discoverycited by 10
- 2023Local Causal Discovery for Estimating Causal Effectscited by 10
- 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
16 more
- 2023Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairscited by 9
- 2024Towards Complete Causal Explanation with Expert Knowledgecited by 7
- 2021A Local Method for Identifying Causal Relations under Markov Equivalencecited by 6
- 2022Causal discovery under a confounder blanketcited by 6
- 2024Local Causal Discovery for Structural Evidence of Direct Discriminationcited by 6
- 2024LLM-initialized Differentiable Causal Discoverycited by 6
- 2022On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inferencecited by 5
- 2023Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networkscited by 5
- 2024Local Causal Discovery with Background Knowledgecited by 5
- 2022Variable elimination, graph reduction and efficient g-formulacited by 4
- 2024Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structurecited by 3
- 2025SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graphcited by 3
- 2025From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?cited by 3
- 2025Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasetscited by 2
- 2025Local Causal Discovery for Statistically Efficient Causal Inferencecited by 2
- 2025Score-Based Causal Discovery with Temporal Background Informationcited by 1
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