发表机构
University of Southern California; University of California, Davis（南加州大学; 加利福尼亚大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对混合数据类型（有序与指数族）的因果图识别问题，证明了边方向的可识别性，并提出了基于评分搜索和DAGMA优化的算法，实验验证了其有效性。
AI 中文摘要
从观测数据中进行因果发现是统计学和机器学习的基础，但在没有干预的情况下确定因果方向需要结构假设。现有的可识别性研究主要关注加性噪声模型下的连续变量，往往忽略了包含有序量表、计数和连续测量的混合数据集。本文研究了有向无环图（DAGs）中的因果发现，其中节点遵循有序分布（通过有序logit模型）或正则单参数指数族分布。我们证明了对于一般参数值，有序节点与指数族节点之间的边方向在分布上是可识别的。我们的结果将先前的Ordinal-Poisson结果推广到更广泛的指数族。在计算方面，我们引入了一种基于评分的穷举搜索和一种使用DAGMA的掩码连续优化框架，用于处理更大的图。数值结果验证了该理论，恢复了在经典结构方程模型下不可识别的马尔可夫等价类中的边方向。
英文摘要
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets containing ordinal scales, counts, and continuous measurements. This paper investigates causal discovery in Directed Acyclic Graphs (DAGs) where nodes follow either an ordinal distribution (via an ordered logit model) or a regular one-parameter exponential family distribution. We prove that the edge direction between an ordinal and an exponential family node is distributionally identifiable for generic parameter values. Our findings generalize previous Ordinal-Poisson results to the broader exponential family. Computationally, we introduce a score-based exhaustive search and a masked continuous optimization framework using DAGMA for larger graphs. Numerical results validate the theory, recovering edge orientations within a Markov equivalence class that are unidentifiable under classical structural equation models.
Comments5 pages, 2 figures, accepted at the 60th Asilomar Conference on Signals, Systems, and Computers 2026
