Abstract:Bayesian causal discovery benefits from prior information elicited from domain experts, and in heterogeneous domains any prior knowledge would be badly needed. However, so far prior elicitation approaches have assumed a single causal graph and hence are not suited to heterogeneous domains. We propose a causal elicitation strategy for heterogeneous settings, based on Bayesian experimental design (BED) principles, and a variational mixture structure learning (VaMSL) method -- extending the earlier differentiable Bayesian structure learning (DiBS) method -- to iteratively infer mixtures of causal Bayesian networks (CBNs). We construct an informative graph prior incorporating elicited expert feedback in the inference of mixtures of CBNs. Our proposed method successfully produces a set of alternative causal models (mixture components or clusters), and achieves an improved structure learning performance on heterogeneous synthetic data when informed by a simulated expert. Finally, we demonstrate that our approach is capable of capturing complex distributions in a breast cancer database.
| Comments: | 32 pages, 19 figures |
| Subjects: | Machine Learning (cs.LG); Methodology (stat.ME) |
| Cite as: | arXiv:2510.06735 [cs.LG] |
| (or arXiv:2510.06735v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.06735 arXiv-issued DOI via DataCite |
From: Jorge Loría [view email]
[v1]
Wed, 8 Oct 2025 07:47:18 UTC (1,566 KB)
[v2]
Wed, 29 Apr 2026 14:18:59 UTC (1,567 KB)
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