因果发现也能讲人话？GENESIS提出可解释方法，给复杂因果推断配上一份清晰说明书，值得研究因果推理的读者细品。
arXiv:2608.03868v1 Announce Type: new Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statisti…
用图思维链改进因果发现，并揭示事后路径公平审计的脆弱性，适合关注AI公平与因果推理的研究者。
arXiv:2608.02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clini…
如何从非结构化数据中发现因果？本文提出利用领域知识增强因果发现的新方法，突破传统局限。
arXiv:2607.09348v1 Announce Type: new Abstract: Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise doma…
智能体时代如何发现因果关系？这篇最新论文给出前沿方法框架。
arXiv:2606.23608v1 Announce Type: new Abstract: Recent attempts to combine large language models (LLMs) with causal discovery ask models to infer pair…
分层因果发现新范式，用LLM动态加权专家模型，提升因果推断准确性与可解释性。
arXiv:2606.10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for rea…
大模型做因果发现时何时可信？这篇论文提出CauTion框架，动态评估LLM的集成信任度，提升因果推断鲁棒性。
arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of p…
因果发现基准测试的一致性评估，揭示现有基准的可靠性问题，为算法选择提供依据。
arXiv:2606.01789v1 Announce Type: new Abstract: In graphical causal model, causal discovery aims to construct a causal graph based on numerical data a…
测试时训练首次应用于监督因果学习，破解分布外泛化难题的新范式。
arXiv:2605.30015v1 Announce Type: cross Abstract: Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised…
该研究证明了LLM在因果发现任务上的根本性失败，并提出介入式代理作为突破路径，是AI推理领域的一道硬核证据。
arXiv:2605.27567v1 Announce Type: new Abstract: Causal discovery is a cornerstone of scientific reasoning, yet whether large language models can perfo…
多智能体协作利用LLM进行因果发现，新方法突破传统因果推断瓶颈
arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental prob…
新型交互式因果发现环境CausaLab，实验揭示预测准确与结构恢复间的显著鸿沟
arXiv:2605.26029v1 Announce Type: new Abstract: We introduce CausaLab, a scalable environment for evaluating interactive causal discovery by LLM agent…
提出TriOpt可扩展算法，破解线性因果发现中图搜索空间超指数爆炸难题。
arXiv:2605.17465v1 Announce Type: new Abstract: Learning causal relations from observational data is challenging because the graph search space grows …