Bayesian causal discovery from unknown general interventions
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Abstract (arXiv)
We consider the problem of learning causal Directed Acyclic Graphs (DAGs) using combinations of observational and interventional experimental data. Current methods tailored to this setting assume that interventions either destroy parent-child relations of the intervened (target) nodes or only alter such relations without modifying the parent sets, even when the intervention targets are unknown. We relax this assumption by proposing a Bayesian method for causal discovery from general interventions, which allow for modifications of the parent sets of the unknown targets. Even in this framework, DAGs and general interventions may be identifiable only up to some equivalence classes. We provide graphical characterizations of such interventional Markov equivalence and devise compatible priors for Bayesian inference that guarantee score equivalence of indistinguishable structures. We then develop a Markov Chain Monte Carlo (MCMC) scheme to approximate the posterior distribution over DAGs, intervention targets and induced parent sets. Finally, we evaluate the proposed methodology on both simulated and real protein expression data.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 1 Dec 2023.
Builds on
15 of 41 references.
References · 15
- 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
- 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
- 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
- 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
- 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
- 2013Causal Discovery from Changescited by 52
- 2013Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 41
- 2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41
- 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
- 2013Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variablescited by 23
- 2013Learning Bayesian Networks: A Unification for Discrete and Gaussian Domainscited by 21
- 2018Direct Estimation of Differences in Causal Graphscited by 14
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.