Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represented in forms of directed acyclic graphs (DAGs). However, existing methods struggle with efficient DAG sampling due to the complex acyclicity constraint. In this study, we propose a scalable Bayesian approach to effectively learn the posterior distribution over causal graphs given observational data thanks to the ability to generate DAGs without explicitly enforcing acyclicity. Specifically, we introduce a novel differentiable DAG sampling method that can generate a valid acyclic causal graph by mapping an unconstrained distribution of implicit topological orders to a distribution over DAGs. Given this efficient DAG sampling scheme, we are able to model the posterior distribution over causal graphs using a simple variational distribution over a continuous domain, which can be learned via the variational inference framework. Extensive empirical experiments on both simulated and real datasets demonstrate the superior performance of the proposed model compared to several state-of-the-art baselines.
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Swiss Federal Technology Institute of Lausanne (EPFL), Switzerland（瑞士联邦理工学院洛桑分校）；Cornell University, USA（康奈尔大学）；ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院）
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Department of Mechanical & Industrial Engineering（机械与工业工程系）；Northeastern University（东北大学）；Department of Industrial Engineering and Management Sciences（工业工程与管理科学系）；Department of Mechanical Engineering（机械工程系）；Northwestern University（西北大学）
Columbia University（哥伦比亚大学）；Irving Institute for Cancer Dynamics（欧文癌症动力学研究所）；Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Department of Statistics, Columbia University（哥伦比亚大学统计系）
Coarsening Linear Non-Gaussian Causal Models with Cycles
Department of Technology, Management and Economics, Technical University of Denmark（技术、管理与经济系，丹麦技术大学）；Department of Mathematical Sciences, University of Copenhagen（数学科学系，哥本哈根大学）
Stable Causal Discovery via Directed Acyclic Graph Aggregation
Yau Mathematical Sciences Center, Tsinghua University（丘成桐数学科学中心，清华大学）；Department of Statistics, University of Virginia（弗吉尼亚大学统计系）；Dr. Bing Zhang Department of Statistics, University of Kentucky（肯塔基大学张炳章统计系）
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）
School of Software, Shandong University（山东大学软件学院）；SDU-NTU Joint Centre for AI Research, Shandong University（山东大学-南洋理工大学人工智能联合研究中心）；Chongqing Uni. of Posts and Telecom.（重庆邮电大学）；MOE Key Laboratory of Big Data Intelligent Computing, Chongqing Uni. of Posts and Telecom.（重庆邮电大学大数据智能计算教育部重点实验室）
Federated Causality Learning with Explainable Adaptive Optimization
School of Software, Shandong University（山东大学软件学院）；SDU-NTU Joint Centre for AI Research, Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）；Departiment of Computer Science, George Mason University（乔治梅森大学计算机系）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
针对隐私保护下分散异构数据的因果发现问题，提出 FedCausal 联邦策略，通过可解释的自适应优化统一局部与全局 DAG 学习，在 non-iid 合成及真实数据上表现优越。
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Booth School of Business, University of Chicago（芝加哥大学布斯商学院）；Carnegie Mellon University（卡内基梅隆大学）
最近，通过利用基于矩阵指数函数的迹的有向无环图（DAGs）的可微无环性特征，从数据中学习有向无环图（DAGs）的组合问题被框架为纯粹的连续优化问题。现有的无环性刻画是基于邻接矩阵的幂包含了行走和循环的信息。在这项工作中，我们提出了一个新的非循环特性的基础上的对数行列式（log-det）功能，它利用了幂零性的DAG。为了处理DAG的固有不对称性，我们将我们的log-det特征域与$\textit{M-matrices}$的集合相关联，这是与在正定矩阵的锥上定义的经典log-det函数的关键区别。类似于以前提出的无环函数，我们的特征也是精确和可微的。然而，与现有的特征相比，我们的log-det函数：（1）更善于检测大周期;（2）具有更好的梯度;（3）它的运行时间实际上快了一个数量级。从优化方面，我们放弃了通常使用的增广拉格朗日计划，并提出DAGMA（$\textit{DAGs via M-matrices for Acyclicity}$），一种类似于障碍方法的中心路径的方法。DAGMA的中心路径中的每个点都是由我们的log-det函数正则化的无约束问题的解，然后我们证明了在中心路径的极限处，该解保证是DAG。最后，我们为$\textit{linear}$和$\textit{nonlinear}$ SEM提供了广泛的实验，并表明我们的方法可以达到大的加速和较小的结构汉明距离对国家的最先…
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
Department of Industrial and Systems Engineering, University of Florida（佛罗里达大学工业与系统工程系）；Department of Biostatistics, MD Anderson Cancer Center（MD安德森癌症中心生物统计学系）
Exploiting Non-Negativity in DAG Structure Learning
Dept. of Signal Theory and Communications, Universidad Rey Juan Carlos（信号理论与通信系，雷昂·卡洛斯大学）；Dept. of Electrical and Computer Engineering, Rice University（电气与计算机工程系，里奇大学）；Dept. of Electrical and Computer Engineering, University of Rochester（电气与计算机工程系，罗切斯特大学）
