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On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated. The results go beyond classical structural equation models as well as results for nodes with observations from a homogeneous family of distributions. The main result establishes that the orientation of every edge joining an ordinal node to an exponential-family node is identifiable from the joint distribution alone at every parameter value, provided the ordinal node has at least three categories and the exponential-family
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T00:01:50.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.