Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms

2026-09-17

AI Summary

研究解决了混合数据集中因果方向识别问题，通过有序logit模型和单参数指数族分布证明了节点间因果方向的可识别性，并提出基于评分的搜索和连续优化框架。

Abstract

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.

Problem

• causal discovery
• observational data
• ordinal distribution
• exponential family distribution
• identifiability

Innovation

• ordinal distribution
• exponential family distribution
• causal discovery
• DAGMA
• mixed datasets