将专家知识融入混合因果图的贝叶斯发现，提升异质数据建模能力。
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
- 基于贝叶斯实验设计构建专家反馈先验，支持多因果图混合学习
- 在异质合成数据上结构学习准确率显著优于传统方法
- 适用于生物医学等复杂异质领域，适合有领域知识的研究者
贝叶斯因果发现可借助领域专家提供的先验信息，在异质领域中尤为关键。然而，现有先验获取方法均假设单一因果图，不适用于异质场景。本文提出一种基于贝叶斯实验设计（BED）的因果先验获取策略，并发展变分混合结构学习（VaMSL）方法，扩展早期可微贝叶斯结构学习（DiBS）框架，用于迭代推断因果贝叶斯网络（CBN）的混合模型。通过整合专家反馈构建信息丰富的图先验，在异质合成数据上成功生成多个备选因果模型（即混合成分或聚类），并实现结构学习性能提升；进一步在乳腺癌数据库中验证了该方法捕捉复杂分布的能力。
原文摘要 · Abstract (English)
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.
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