用熵推断构建因果图谱，揭示数据中隐藏的多种合理因果关系。
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
- 基于熵最大化生成多组可能的因果图，避免单一最优解偏差
- 在20节点线性模型上验证了因果结构存在显著不确定性
- 适合研究复杂系统中多重因果路径的科研人员
数据驱动的因果关系识别对理解复杂系统至关重要。贝叶斯网络通过有向无环图（DAG）提供建模通用因果关系的概率方法。然而，传统构造贝叶斯网络的方法依赖优化，可能不适用于学习因果关系，因为底层数据可能允许多条因果链。更符合数据特性的因果表示可提供框架，用于构建与数据内在变异性一致的多个因果地图。本文表明，基于熵的推断可生成与底层数据一致的潜在因果关系图谱。在2节点和20节点线性结构方程模型的模拟噪声数据上，我们采样了最大熵图集，量化了底层因果关系中的固有结构模糊性。结果表明，‘优化’后的DAG可能包含不一致于同等精度拓扑的因果伪影。
原文摘要 · Abstract (English)
Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relationships via directed acyclic graphs (DAGs). However, typical techniques for constructing Bayesian networks rely on optimization, which can be ill-suited for learning causal relationships because the underlying data may admit multiple chains of causation. More data-faithful representations of causal relationships would provide frameworks for constructing multiple causal maps that are consistent with the variability that is inherent in underlying data. Here, we show that entropy-based inference generates atlases of plausible causal relationships that are consistent with underlying data. On simulated noisy data of 2- and 20-node linear structural equation models, we sample a maximum-entropy ensemble of graphs that allow us to quantify the inherent structural ambiguity in underlying causal relationships. Our method shows that "optimized" DAGs can contain causal artifacts are not consistent across equivalently accurate topologies.
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