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论文提出受控证据聚合（CEA）与平衡效用因果选择（BUC）方法，通过符号探针定位出可影响Transformer自然语言决策的紧凑注意力头集合，并在Pythia-410M上验证其因果效应。
该论文研究预训练模型容量如何影响因果任务的适应效果，基于 Qwen 2.5 构建了 CausalLLM（标量势结果估计）和 CausalGen（生成式反事实建模）两个系统，探讨从标量预测到反事实后训练的容量-适应对齐问题，并被 NeurIPS 2026 Workshop 接收。
该论文将常见的对比式和探针式激活 steering 方法统一为结果回归平均梯度的估计器，并利用去偏机器学习中的 Neyman 正交性与 Stein score 构造双重稳健的新估计器，在真实性、概念引导等数据集上提升了 steering 性能。
Neural-DISCO 提出一种解纠缠表示学习框架，用于神经群体活动的源条件反事实编辑：可替换单个行为或刺激标签的潜在分量，同时保留其他标签与源特异性变异性，并结合特征归因识别单神经元层面的功能角色。
该论文审计了 1999 至 2026 年间的 113 项研究，发现只有 9 项主要评测真正测量了真实人类对系统所选行动的响应，其中仅 2 项还将显式的响应预测与实测响应进行比较；在 25 项语言模型相关工作的全文检查中没有发现符合这些条件的实验。作者提出了一套直接预测评测标准，并设计了一个用于谈判助手的单步实验来区分任务成功、当前状态推断与真实响应预测。
A preregistered causal audit finds that covariate-aware time series foundation models (Chronos-2, TiRex-2, TabPFN-TS, TimesFM-2.5) forecast associations rather than intervention effects, with a derived confounding gap that can be reduced up to 83% via brief interventional post-training with LoRA adapters.
Yale researchers establish causal upper and lower bounds on CoT monitorability metrics, showing that the minimal-criterion TPR metric can overestimate intervention-caused behavior detection, and propose three intervention-aware post-training methods (off-policy self-distillation, RL, on-policy self-distillation) that improve LLM reasoning-trace monitorability.
该论文研究前沿语言模型的概率判断是否满足全概率律（含因果 do-演算的后门调整），发现 GPT-6.1 Sol 和 Claude Opus 5.5 最一致，而推理长度与一致性无关，且一致性残差可在无真值情况下标记不可靠回答。
This arXiv paper explores realistic models of change through a causal perspective, connecting decision-making frameworks with dynamical systems to better capture how systems evolve over time.
The paper proves that the closed-form bounds proposed by Shu et al. for probabilities of causation are sharp in every dimension, and shows that PNS, PSub, PRep, and PN are all special cases of a single unified theorem for discrete PoCs.
本文提出 D-VAE（Deconfounder Variational Autoencoder）因果推断框架，用于评估 LLM 生成广告中专家营销标签的因果价值，并在 29,177 个化妆品广告创意上验证其效果，实验显示推荐配置可提升 32.7% 的用户互动。
该论文提出 ECI2D 方法，通过将语言推理链抽象为可执行程序并施加因果干预（do-operator）来筛选自生成的上下文示例，解决仅靠观察过滤示例时的观测等价问题，在数学、逻辑与常识推理基准上取得显著提升。
CausalEvo: Causal Skill Self-Evolution for Health LLM Agents 提出了一种因果技能自进化方法，通过在固定模型权重和规则数量下对比不同技能集的效果来决定保留或替换技能，在 MedCalc-Bench-Verified 六项任务上达到 60.00% 准确率，优于 SkillOpt（55.83%），且训练 token 消耗减少 55.6%。
The paper introduces CoBe, a benchmark of 2000 questions evaluating models' zero-shot ability to perform conversational counterfactual text editing, finding that frontier models (averaging 58.05%) often default to associational generation rather than preserving causal structure, with performance not scaling with model size or token length.
提出 Causal Market Simulator：一种基于因果先验训练的连续时间 Prior-Data Fitted Network，用于估计交易的因果市场冲击，显著缓解了经典 Almgren–Chriss 等回归模型在信息性流导致混杂时的偏差，并在模拟订单簿及 BTC/ETH/SOL 永续合约与 Nasdaq 股票数据上展示了正向的因果迁移证据。
The paper introduces ReRoute, a framework for counterfactual 'what-if' prediction in scientific emulators that combines factual data with partial mechanistic knowledge—without needing controlled intervention data—achieving 18.2–31.8% lower climate error under CO2 distribution shifts. Its causal identification result is machine-checked in Lean, with applications to advection–diffusion systems, coupled-climate emulation, and ERA5-trained emulators.
The paper audits attention-head ablation in language models, showing that naive output-level ablation mixes heads via projection and yields nearly uncorrelated results versus corrected pre-projection ablation, while also demonstrating floor/ceiling effects in accuracy and the need for matched controls on held-out data for defensible causal claims.
A paper on learning causal structure in linear Gaussian structural causal models with directed cycles and latent confounders, introducing marginal quasi-equivalence and a differentiable Bernoulli-gate parameterization with a consistent complexity penalty.
This paper characterizes when regression is possible under sample selection biases that depend on both covariates and outcomes, showing the regression function can be identified even when the selection filter is not. It provides finite-sample estimation guarantees, oracle-efficient algorithms, and the first general-purpose estimation method for this class of selection problems.
This paper frames recovery in LLM agent harnesses as a causal decision problem, separating rescue from harm outcomes, and introduces the Causal Intervention Router (CIR), which raises Qwen3-14B success on long-horizon ALFWorld tasks from 70.33% to 73.33% by selectively triggering environment refresh.