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Causal discovery needs explicit epistemic standards — arXiv:2611.03166

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03166
License (as archived): CC0-1.0
Archived: 2026-11-03T11:26:42.471Z
SHA-256 of the archived text: 485a3299dbea4d742200bfaef399213d72ddbbe3b37ebc3b764c53e9281caf42
Stable URL of this page: https://metraipolis.com/documento/dc_893c8cdf034d

How to cite

Causal discovery needs explicit epistemic standards — arXiv:2611.03166 — arXiv (en), https://arxiv.org/abs/2611.03166 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_893c8cdf034d (Metraipolis archive dc_893c8cdf034d, sha256 485a3299dbea4d742200bfaef399213d72ddbbe3b37ebc3b764c53e9281caf42)

BibTeX

@misc{dc893c8cdf034d,
  title = {Causal discovery needs explicit epistemic standards — arXiv:2611.03166},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03166},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_893c8cdf034d; sha256 485a3299dbea4d742200bfaef399213d72ddbbe3b37ebc3b764c53e9281caf42},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03166. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

There are many causal discovery algorithms, but few guiding principles for choosing between them. Methods are usually compared through three lenses: identifiability, consistency, and benchmarks. Each introduces assumptions about the data-generating process that are often untestable. We argue that there are no shared standards for comparing, prioritizing, or even discussing those assumptions. We support this claim by showing that (i) identifiability assumptions are often preferred by convention rather than by principle, (ii) uniform consistency requires further untestable assumptions, while the weaker pointwise consistency has arguably narrowed algorithm design, and (iii) benchmarks are not neutral tests but carry an epistemic role of their own: they quietly fix which regimes matter, which guarantees come into play, and what counts as success. In doing so, they shape what algorithm comparisons are taken to show and thus perform some of the same epistemic work as theory, but less transparently. In this position paper we argue for a more transparent, integrated practice: Make the epistemic standards for discussing identifiability and consistency assumptions explicit, especially their failure modes and scope conditions; design benchmarks to operationalize those standards, by stress-testing and ablating both. Then, theoretical and empirical results jointly clarify when, why, and how algorithms may work rather than accumulate as disconnected advances.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2611.03166 — 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 2026-11-03T11:02:37.901Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).