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CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Paper recorded by Signals 4 on 2026-09-10 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.LG · 机器学习 · first seen 2026-09-11

Abstract

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

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#70 most recent of 215 cs.LG papers we have recorded · ↑ newer: TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Tra · ↓ older: 3D Point Splatting for mmWave Radar Novel View Synthesis
Cite this page: CausalArena: Benchmarking Causal Discovery in the Foundation Model Era: the #70 most recent of 215 cs.LG papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/causalarena-benchmarking-causal-discovery-in-the-foundation-model-era.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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