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arXiv Machine Learning By Sambit Mishra, Urbashi Mitra

On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models

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The paper investigates identifiability in linear parametric models where variables follow either an ordered logit or a one‑parameter exponential family distribution. It proves that the direction of every edge linking an ordinal node to an exponential‑family node can be determined from the joint distribution, provided each node has at least three categories or support points, respectively. Numerical experiments confirm that these orientations can be distinguished even when conditional independence tests cannot separate them.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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