A new research paper published on arXiv explores the identifiability of causal directed acyclic graphs (DAGs) within linear parametric models. The study focuses on scenarios where nodes in the DAG follow either an ordered logit model or a regular one-parameter exponential family distribution. The key finding is that edges between ordinal nodes with at least three categories and exponential-family nodes with at least three points of support are identifiable from the joint distribution alone. This research extends beyond traditional structural equation models and provides a theoretical framework for distinguishing causal relationships in mixed-distribution settings, supported by numerical experiments. AI
IMPACT Advances theoretical understanding of causal inference in machine learning models.
RANK_REASON Academic paper on a theoretical machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- exponential family
- Hugging Face
- Linear Parametric Models
- machine learning
- Markov Equivalence Class
- Ordered LOGIT Model approach for the determination of financial distress
- Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction
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