No AI summary available for this article.
Why It Matters
Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generate data, yet existing studies differ substantially in graph families, mechanisms, and evaluation protocols. The emergence of causal discovery foundation models (CDFMs) further complicates evaluation: performance may reflect not only causal discovery ability, but also overlap between pretraining environments and test SCM...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.11897v1 · Indexed about 3 hours ago