Metraipolis › archive › dc_efeb61e030c2

Decoupled Causal Discovery — arXiv:2609.23535

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23535
License (as archived): CC0-1.0
Archived: 2026-10-09T07:07:15.146Z
SHA-256 of the archived text: 73c81ecb70218d685cd0faa4d9b6198aa6733d0e4f868376ce0ad22cae84af9f
Stable URL of this page: https://metraipolis.com/documento/dc_efeb61e030c2

How to cite

Decoupled Causal Discovery — arXiv:2609.23535 — arXiv (en), https://arxiv.org/abs/2609.23535 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_efeb61e030c2 (Metraipolis archive dc_efeb61e030c2, sha256 73c81ecb70218d685cd0faa4d9b6198aa6733d0e4f868376ce0ad22cae84af9f)

BibTeX

@misc{dcefeb61e030c2,
  title = {Decoupled Causal Discovery — arXiv:2609.23535},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23535},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_efeb61e030c2; sha256 73c81ecb70218d685cd0faa4d9b6198aa6733d0e4f868376ce0ad22cae84af9f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23535. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspective that does not rely on these methodologies. DCD directly identifies the Markov boundary (MB) by decoupling non-target variables via weighting functions, such that only variables within the MB preserve dependence with the target under the decoupled distribution. Building on this, DCD iteratively constructs the Completed Partially Directed Acyclic Graph (CPDAG) by exploiting structural asymmetries within the MBs. We establish the theoretical identifiability, soundness, and completeness of DCD. Empirical evaluations demonstrate that DCD achieves strong performance, particularly excelling in challenging noise regimes.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.23535 — 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 2026-10-09T06:58:09.309Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).