SOURCE-LINKED INTELLIGENCE

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global