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Demistifying Data and Simulator Assumptions in Supervised Causal Discovery — arXiv:2609.37446

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37446
License (as archived): CC0-1.0
Archived: 2026-10-07T16:23:52.703Z
SHA-256 of the archived text: bc83f58fd5e00536a8589b138a38bf77e7e398f1dfbda235cc9a497c61b3dd67
Stable URL of this page: https://metraipolis.com/documento/dc_ebc368dbe4fa

How to cite

Demistifying Data and Simulator Assumptions in Supervised Causal Discovery — arXiv:2609.37446 — arXiv (en), https://arxiv.org/abs/2609.37446 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebc368dbe4fa (Metraipolis archive dc_ebc368dbe4fa, sha256 bc83f58fd5e00536a8589b138a38bf77e7e398f1dfbda235cc9a497c61b3dd67)

BibTeX

@misc{dcebc368dbe4fa,
  title = {Demistifying Data and Simulator Assumptions in Supervised Causal Discovery — arXiv:2609.37446},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37446},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebc368dbe4fa; sha256 bc83f58fd5e00536a8589b138a38bf77e7e398f1dfbda235cc9a497c61b3dd67},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37446. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pairs are typically simulated, making the simulator both a source of supervision and a carrier of assumptions about causal graphs, mechanisms, and noise. Understanding the resulting predictions therefore requires examining how these assumptions supplement the information available in observational data, which may be compatible with multiple causal graphs. This paper examines that relationship across representative methods available through June 2026. We organize these methods by prediction target, prediction granularity, encoder, structural decoder, and training regime to relate what each method predicts to how it uses data and simulator-based supervision. Using this framework, we distinguish two questions: whether the target is identifiable under the assumed model class, and whether a trained predictor generalizes beyond its training distribution. Restrictions on mechanisms and noise can make otherwise ambiguous causal directions identifiable, but predictive accuracy under those restrictions does not establish transfer when they change. This distinction motivates evaluation that matches metrics to the identifiable graph target and tests changes in graphs, mechanisms, and noise between training and deployment. Extending such evaluation to real data also requires documenting the external causal evidence and uncertainty behind benchmark reference graphs. Together, these analyses guide method comparison and identify open questions in transfer, test-time adaptation, and uncertainty assessment.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.37446 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T15:54:53.626Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).