用因果推断自动发现高效可解释的LLM多智能体通信拓扑，减少冗余交互、提升任务表现，值得关注。
arXiv:2608.12921v1 Announce Type: cross Abstract: The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on eff…
受限Logit模型如何在大规模场景精准估算需求转移系数，为零售决策提供关键依据。
arXiv:2608.12680v1 Announce Type: new Abstract: Item demand forecasting is an integral component of store assortment optimization. Existing literature…
大模型理解因果难题？这个智能体框架专攻因果推理，让LLM真正掌握因果逻辑
arXiv:2408.06849v3 Announce Type: replace Abstract: The large language model (LLM) has achieved significant success across various domains. However, t…
用因果推断给数字孪生做“证伪测试”，让仿真模型不再自说自话。
arXiv:2301.07210v5 Announce Type: replace-cross Abstract: Digital twins are simulation-based models designed to predict how a real-world process will …
针对大模型因果推理短板，探究后训练能否带来质的飞跃，实验结论颇具启发。
arXiv:2602.06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts. W…
多通道场景下如何精准优化增量收益？这项研究给出可落地的策略学习方法，适合因果推断与增长算法从业者。
arXiv:2607.28182v1 Announce Type: new Abstract: E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize busine…
用对抗不确定性的方式引导LLM生成语义增强数据，大幅提升异质处理效应估计精度
arXiv:2607.26599v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized …
用因果推断揭示AI编程助手如何重塑开源社区，新手参与门槛是否真正降低？实证研究给出答案。
arXiv:2607.01810v1 Announce Type: cross Abstract: Open-source projects depend on a steady inflow of newcomers. A growing concern is that AI coding age…
用因果推断视角优化数据混合，提升语言模型训练效率与效果的创新方法。
arXiv:2607.01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model perfor…
提出RetiSEM框架，破解碎片化生物医学数据中的因果建模难题，为精准医疗提供新思路。
arXiv:2606.24488v1 Announce Type: cross Abstract: Learning causal models from fragmented biomedical data is challenging because clinical, molecular, a…
这篇论文从机制层面探究大模型检测自杀内容的内在工作原理，并提出让因果断言更可信的方法，专业深度十足。
arXiv:2606.21078v1 Announce Type: new Abstract: Large language models are increasingly proposed for mental-health applications such as detecting suici…
因果图未知时如何保证公平？这项研究在变量簇上实现了干预公平，为AI可靠性提供新解法。
arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but a…
反因果视角突破域泛化瓶颈，巧用未标注数据提升模型泛化能力，ICML 2026前沿研究
arXiv:2602.17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to …
用生成式AI破解文本作为干预变量的因果推断难题，理论框架与实操方法兼备。
arXiv:2410.00903v5 Announce Type: replace-cross Abstract: In this paper, we demonstrate how to enhance the validity of causal inference with unstructu…
探讨统计决策理论中引入反事实损失的新框架，为因果推断与机器学习决策提供理论支撑。
arXiv:2505.08908v3 Announce Type: replace-cross Abstract: Many researchers apply classical statistical decision theory to evaluate treatment choices a…
探讨新闻话题情感是否影响人们对政治立场的感知，并对比人与LLM的标注差异，揭示大模型在意识形态推断上的潜力与局限。
arXiv:2606.06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the …
KDD 2026论文提出针对纵向数据中罕见事件的可扩展反事实风险估计方法，解决因果推断与高维数据挑战。
arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational …
ICML'26接收，新方法通过协变量转移建模高效估计随机实验纵向治疗效果。
arXiv:2605.31443v1 Announce Type: cross Abstract: We present a regression-adjustment framework designed for the estimation of longitudinal treatment e…
纵向数据下多策略因果效应平滑估计，提升精度与可解释性，适合因果推断研究者。
arXiv:2605.14284v2 Announce Type: replace Abstract: Comparative evaluation of multiple dynamic treatment policies is essential for healthcare and poli…
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…