$χ$SPN：面向混合域因果推断的特征干预和积网络

测试时从干预数据中学习因果结构

Test-Time Learning of Causal Structure from Interventional Data

TICL通过结合测试时训练与联合因果推断，有效解决干预数据中因果结构学习的泛化问题，提升因果发现和干预目标检测性能。

Observational and Interventional Causal Learning for Regret-Minimizing Control

We explore how observational and interventional causal discovery methods can be combined. A state-of-the-art observational causal discovery algorithm for time series capable of handling latent confounders and contemporaneous effects, called LPCMCI, is extended to profit from casual constraints found through randomized control trials. Numerical results show that, given perfect interventional constraints, the reconstructed structural causal models (SCMs) of the extended LPCMCI allow 84.6% of the time for the optimal prediction of the target variable. The implementation of interventional and obse…

基于模拟器的时序因果发现：通过模拟器生成的干预分布

Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions

本文提出SVAR-FM框架，利用物理模拟器实现Pearl的do操作，通过物理约束消除混杂路径，利用条件流匹配学习非线性干预条件，理论证明结构VAR在覆盖条件下可识别，并通过四个科学领域实验验证其有效性。

On the Interventional Kullback-Leibler Divergence

Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intelligent agent needs to be able to transfer knowledge and re-use learned components across domains. It has been argued that this may be possible through causal models, aiming to mirror the modularity of the real world in terms of independent causal mechanisms. However, the true causal structure underlying a given set of data is generally not identifiable, so it is desirable to have means to quantify differences between …

学习灵活的时间窗Granger因果关系并整合异构干预时间序列数据

Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data

本文针对未知干预目标下的Granger因果推断问题，提出一种利用异构干预时间序列数据同时学习因果结构和识别干预目标的理论方法，并证明两者可相互促进，实验表明其优于多个稳健基线方法。

RealTCD：利用大语言模型从干预数据中进行时序因果发现

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model

针对工业场景中无干预目标且含文本信息的时序因果发现难题，提出RealTCD框架，结合基于评分的方法与LLM引导的元初始化，在模拟和真实数据上优于现有基线。

Combining Interventional and Observational Data Using Causal Reductions

Unobserved confounding is one of the main challenges when estimating causal effects. We propose a causal reduction method that, given a causal model, replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that takes values in the same space as the treatment variable, without changing the observational and interventional distributions the causal model entails. This allows us to estimate the causal effect in a principled way from combined data without relying on the common but often unrealistic assumption that all confounders have been observ…

干预时间序列先验用于因果基础模型

Interventional Time Series Priors for Causal Foundation Models

本文提出CausalTimePrior框架，生成合成时间结构因果模型，支持可配置因果图、非线性自回归机制和多干预类型，推动时间序列因果推断的基础模型发展。

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that research…

CAnDOIT：利用时间序列的观测和干预数据进行因果发现

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series

本文提出CAnDOIT方法，结合观测和干预时间序列数据重构因果模型，解决隐藏因素下仅靠观测数据难以识别因果关系的问题，并在合成数据和机器人操作基准上验证其能有效提升因果分析准确性。

认证观测等价因果模型间的干预一致性

Certifying Interventional Agreement Among Observationally Equivalent Causal Models

本文提出干预分离选择（ISS）方法，通过查询干预来认证观测等价因果模型在干预下的一致性，并在连续变量上精确判定停止条件，实验证明其能有效审计网络因果抽象。

Causal Adversarial Network for Learning Conditional and Interventional Distributions

We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph to be given, our proposed framework learns the causal relations from the data and generates samples accordingly. The proposed CAN comprises a two-fold process namely Label Generation Network (LGN) and Conditional Image Generation Network (CIGN). The LGN is a GAN-based architecture which learns and samples from the causal model over labels. The sampled labels are then fed to CIGN, a conditional…

因果变分深度嵌入：一类针对混杂图像的可干预生成器

A Causal Bootstrap for Diverse Image Generation under Unobserved Confounding

提出CauVaDE，基于规范增广SCM将未观测混杂因子分解为离散潜变量，通过熵正则化追踪可行干预分布族，在图像数据上生成多样化干预样本并提升FID。

PACER：从大规模干预数据中进行无环因果发现

PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

PACER通过构建无环性保证的因果发现框架，在大规模高维干预数据中实现高效且准确的因果结构推断，优于现有方法。

Interventional Experiment Design for Causal Structure Learning

It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large portion of the edges will be remained unidentified. The golden standard for learning the causal DAG beyond Markov equivalence is to perform a sequence of interventions in the system and use the data gathered from the interventional distributions. We consider a setup in which given a budget $k$, we design $k$ interventions non-adaptively. We cast the problem of finding the best intervention target set as an optimization p…

通过因果归因进行实时灾害评估的干预方法

An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution

本文提出一种基于因果归因的实时灾害评估干预工具，利用卫星图像、新闻和社交媒体等实时数据源，支持反事实场景模拟、因果归因分析及缓解规划。

因果神经概率电路

Causal Neural Probabilistic Circuits

提出因果神经概率电路（CNPC），通过结合神经属性预测器和从因果图编译的因果概率电路，支持遵循因果依赖的精确干预推理，从而提升概念瓶颈模型在干预下的分类准确率。

ACTIVA: 基于Transformer变分自编码器的摊销因果效应估计

ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders

提出ACTIVA模型，利用Transformer条件变分自编码器从观测数据和干预查询中摊销估计完整干预分布，实现零样本推理，并在合成和基因表达数据上优于基线。

利用干预数据的大规模贝叶斯因果发现

Large-Scale Bayesian Causal Discovery with Interventional Data

针对大规模因果发现任务性能差、无法量化不确定性的问题，提出IBCD经验贝叶斯框架，通过对总因果效应矩阵建模、设置尖峰-板条马蹄先验实现不确定性感知推理，在仿真和521基因Perturb-seq数据上均取得更优的结构恢复效果。

干预式因果表示学习

Interventional Causal Representation Learning

本文研究干预数据对因果表示学习的促进作用，证明完美do干预可实现潜在因子在置换与缩放意义下的可识别性，非完美干预可实现块仿射识别，且无需对分布或依赖结构作任何假设。

引入干预独立性以提高对干预分布偏移的鲁棒性

Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift

本文提出RepLIn算法，通过强制干预表示之间的统计独立性，提高模型对干预分布偏移的鲁棒性。

非独立同分布图数据上的因果公平节点分类

Causally Fair Node Classification on Non-IID Graph Data

针对图数据中节点因果机制异质性违反经典因果模型假设的问题，提出基于网络结构因果模型的消息传递变分自编码器，通过计算干预分布实现因果公平节点分类，理论保证可分解性和图独立性条件，实验验证其有效缓解偏差。

具有潜在混杂因素和后处理选择的干预数据中的因果图表征与学习

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data

本文提出了一种新的因果公式，通过建模后处理选择和干预反应，识别因果关系、潜在混杂因素和选择模式，提升因果发现的准确性。

具有收敛性和最优性保证的图模型干预因果结构发现

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees

针对观测与干预数据融合及集中式因果发现的通信、隐私问题，提出带收敛与最优性保证的Bloom双层多项式优化框架，并扩展至分布式场景，实验显示其性能优于主流算法。

图不确定性下的公平性：在变量簇的部分已知因果图上实现干预公平性

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

提出一种利用变量簇因果图实现干预公平性的学习框架，通过调整簇集间的最大均值差异训练模型，在有限因果图知识下平衡公平与精度。

Causal Reinforcement Learning using Observational and Interventional Data

Learning efficiently a causal model of the environment is a key challenge of model-based RL agents operating in POMDPs. We consider here a scenario where the learning agent has the ability to collect online experiences through direct interactions with the environment (interventional data), but has also access to a large collection of offline experiences, obtained by observing another agent interacting with the environment (observational data). A key ingredient, that makes this situation non-trivial, is that we allow the observed agent to interact with the environment based on hidden informatio…

因果不确定性量化的干预过程

Interventional Processes for Causal Uncertainty Quantification

本文提出一种基于高斯过程的方法，通过将干预函数表示为再生核希尔伯特空间中观测函数的内积，实现干预函数的不确定性量化，并给出闭式后验矩和可处理的训练推理过程。

Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical) changes in the data-generating process, whether due to natural reasons or by the action of an adversary. We consider causal Bayesian networks and formally define the interventional robustness problem, a novel model-based notion of robustness for decision functions that measures worst-case performance with respect to a set of interventions that denote changes t…

隐变量因果模型中干涉分布的一种有效算法

An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models

图模型中的概率推理是从联合概率分布的因子分解表示计算感兴趣的边际和条件密度的任务。诸如变量消除和置信传播之类的推理算法利用嵌入在该因子分解中的约束来有效地计算这样的密度。在本文中，我们提出了一个算法，计算由无环有向混合图（ADMG）表示的潜在变量因果模型的干预分布。为了有效地计算这些分布，我们利用递归因式分解，它概括了通常的马尔可夫分解的DAG和最近的因式分解的ADMG。我们的算法可以被看作是一个推广的变量消除的混合图的情况下。我们表明，我们的算法是指数的混合图推广的树宽。

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks. While various approaches aiming towards this goal have been proposed in recent times, a commonly accepted definition and validation procedure is missing. We provide a causal perspective on representation learning which covers disentanglement and domain shift robustness as special cases. Our causal framework allows us to introduce a new metric for the quantitative evaluation of deep latent variable …

Differentiable Causal Discovery from Interventional Data

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interven…

部分已知因果图上的介入式公平性：一种约束优化方法

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach

针对现有因果公平方法依赖完全已知因果图的局限，提出基于PDAG的约束优化框架，在因果图部分已知时平衡公平性与预测准确率，经模拟和真实数据集验证有效。

认证干预保真度：在机制可解释性中对因果声明进行随时有效的自适应评估

Certified Interventional Fidelity: Anytime-Valid, Adaptive Evaluation of Causal Claims in Mechanistic Interpretability

研究机制可解释性中因果声明评估问题，提出认证干预保真度（CIF）方法，通过将报告量写为因果估计量，提供置信区间等，能验证高保真声明，明确方法差异及对干预分布的敏感性，还降低了认证成本。

面向因果发现的自适应在线实验设计

Adaptive Online Experimental Design for Causal Discovery

针对现有因果发现方法依赖无限干预数据的问题，本文从在线学习视角提出基于分配匹配的自适应追踪-停止因果发现算法，在仿真中以更少样本实现更高准确率。

TabCausal: 跨因果环境的表格因果发现预训练

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

提出TabCausal，一种通过动态任务构建策略在多样化因果环境中进行大规模预训练的因果发现基础模型，在合成和语义基准上优于现有方法。

用于鲁棒且可解释的效应预测的因果干预预测系统

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting

针对AI预测系统因隐藏偏差和缺失信息而脆弱的问题，提出基于变分自编码器和多重插补的因果干预预测系统（CIPS），通过构建因果图分析潜在因果性，在鲁棒性、可解释性和实际性能上超越现有方法。

基于未知多节点干预的线性因果表征学习

Linear Causal Representation Learning from Unknown Multi-node Interventions

研究未知多节点干预下的线性因果表征学习，证明软干预可达祖先级辨识、硬干预可完全辨识，并给出实现该保证的算法。

学习未知因果干预的混合模型

Learning Mixtures of Unknown Causal Interventions

针对干预数据生成受噪声干扰、呈现混合分布的问题，该研究在未知因果图的线性高斯SEMs中，证明do与软干预的分布特性可解耦混合数据，明确样本复杂度与干预强度负相关，可识别到干预马尔可夫等价类，并通过模拟验证了理论结果。

基于扩散模型建模因果机制以处理干预与反事实查询

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries

针对仅含观测数据与因果图的因果充分场景下的多类因果查询问题，提出基于扩散的因果模型学习因果机制，通过潜在编码实现干预采样与反事实溯因，性能优于现有方法并提供通用理论分析框架。

VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries

In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs…

Size of Interventional Markov Equivalence Classes in Random DAG Models

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and ordering an Erdős-Rényi graph. For constant density, we show that the expected $\log$ observational MEC size asymptotically (in the number of vertices) approaches a constant. We characterize I-MEC size in a similar fashion in the above settings with high precision. We show that th…

从边缘干预数据估计联合干预分布

Estimating Joint Interventional Distributions from Marginal Interventional Data

本文提出利用最大熵原理从边缘干预数据估计所有变量的联合条件分布，通过扩展因果最大熵方法，结合干预和观察数据，实现因果特征选择和联合干预分布推断。

Interventional Few-Shot Learning

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-sh…

证据类型竞争：干预数据何时能教会语言模型因果方向？

Evidence-Type Competition: When Can Interventional Data Teach Language Models Causal Direction?

该研究发现干预数据训练语言模型因果推理的金标准假设存在局限，观测上下文会抑制模型的因果方向判断能力，提出证据平均方案可降低符号错误率。

DoFlow：基于流的生成模型用于时间序列的干预和反事实预测

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series

DoFlow通过基于因果图的流模型实现时间序列的干预和反事实预测，并提供异常检测能力。

单细胞显微镜中的干预式风格迁移实现分布外泛化

Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy

针对单细胞显微镜图像，提出干预式风格迁移（IST）方法，通过生成干预式训练分布缓解虚假相关，显著提升分布外泛化，并发布代码与数据集。

从单变量干预中推导因果序：理论保证与算法

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm

针对单变量干预数据因果结构学习的应用难题，提出新干预忠实性变体与因果序评分，证明理论保证，设计Intersort算法，在模拟基准中表现优于现有基线方法。

物理模拟器作为do-运算符：在潜在混杂因素下的因果发现用于科学人工智能

Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science

本文提出CFM-SD方法，利用物理模拟器作为do-运算符，解决AI-for-Science中潜在混杂因素和真实干预数据的因果发现问题，实验表明其在合成数据和真实科学数据中均表现出色。

从非干预复杂系统中解码干预性动力学因果性

Deciphering interventional dynamical causality from non-intervention complex systems

本文提出IntDC框架和IEE算法，通过延迟嵌入空间在不需干预或动力学模型的情况下，从观测数据中解码因果性，验证了其在因果分析中的有效性。

硬干预下因果图的特征化与学习

Characterization and Learning of Causal Graphs from Hard Interventions

本文研究硬干预下的因果图学习，提出基于do-calculus的图形约束刻画带潜变量因果图的干预等价类，并设计整合多干预数据集的学习算法，证明了其可靠性。

加权因果有向无环图的新度量与搜索算法

New metrics and search algorithms for weighted causal DAGs

本文针对带节点成本的因果图自适应干预发现问题，提出新的最坏情况成本基准，并设计在原子、有界规模及广义成本设置下达到对数近似的搜索算法。

基于李括号几何的潜在混杂因果发现

Latent Confounded Causal Discovery via Lie Bracket Geometry

利用信息几何和范畴论，提出两种算法（BRIDGE和SKFM），通过干预诱导流的李括号非闭合性检测潜在混杂，大幅缩减因果图搜索空间。

基于干预的因果图样本高效贝叶斯学习

Sample Efficient Bayesian Learning of Causal Graphs from Interventions

针对有限且昂贵的干预样本，本文提出贝叶斯因果图学习算法，利用多项式时间均匀 DAG 采样枚举切割配置并依据后验选择干预目标，在理论保证和模拟实验中均优于基线。

SCOUT：在软干预下未知目标的循环因果发现

SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

SCOUT提出一种新框架，通过最大化数据对数似然恢复图结构，利用合同残差流和神经样条流，实现从软干预数据中学习非线性循环因果关系，优于现有方法。