Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the server, or even the data collector, cannot be trusted. The introduction of noise, however, inevitably affects the utility of the data, particularly by distorting the correlations between individual data components. This distortion can prove detrimental to tasks such as causal discovery. In this paper, we consider various well-known locally differentially private mechanisms and compare the trade-off between the privacy they provide, and the accuracy of the causal structure produced by algorithms for causal learning when applied to data obfuscated by these mechanisms. Our analysis yields valuable insights for selecting appropriate local differentially private protocols for causal discovery tasks. We foresee that our findings will aid researchers and practitioners in conducting locally private causal discovery.
The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
School of Mathematics, Southeast University, Nanjing 210096, China（数学学院，东南大学，南京210096，中国）；School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China（网络科学与工程学院，东南大学，南京210096，中国）
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
University of Houston（德克萨斯大学休斯顿分校）；NEC Laboratories America（NEC美国实验室）；Florida International University（佛罗里达国际大学）；North Carolina State University（北卡罗来纳州立大学）
Can Large Language Models Help Experimental Design for Causal Discovery?
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Hong Kong Baptist University（香港浸会大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Linear Causal Discovery with Interventional Constraints
纳入因果知识和机制对于完善因果模型和改进下游任务（例如设计新治疗方法）至关重要。在本文中，我们引入了一个新的概念，在因果发现，称为介入约束，这从根本上不同于介入数据。虽然干预性数据需要变量的直接扰动，但干预性约束以因果效应的不等式约束的形式对高层次的因果知识进行编码。例如，在Sachs数据集中（Sachs et al.\ 2005年），Akt已被证明是由PIP3激活，这意味着PIP3对Akt发挥积极的因果作用。现有的因果发现方法允许强制执行结构约束（例如，需要从PIP3到Akt的因果路径），但它们仍然可能产生不正确的因果结论，例如学习“PIP3抑制Akt”。干预约束通过明确约束变量对之间的总因果效应来弥合这一差距，确保学习模型尊重已知的因果影响。为了形式化干预约束，我们提出了一个度量来量化线性因果模型的总因果效应，并将问题制定为约束优化任务，使用两阶段约束优化方法求解。我们在真实世界的数据集上评估了我们的方法，并证明了整合干预性约束不仅提高了模型的准确性，确保了与已有发现的一致性，使模型更易于解释，而且还有助于发现新的因果关系，否则识别成本很高。
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Tsinghua University（清华大学）；Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS)（清华大学脑与认知科学研究院（THUIBCS））；Chinese PLA General Hospital（中国人民解放军总医院）
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
University of California San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；KDDI Research（KDDI研究所）
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Guangdong University of Technology（广东工业大学）；University of Melbourne（墨尔本大学）；Shiga University（滋贺大学）；RIKEN（理化学研究所）
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Technical University of Munich（慕尼黑工业大学）；Mila - Quebec AI Institute（米拉 - 魁北克人工智能研究所）；ServiceNow Research（ServiceNow研究院）；Université de Montréal（蒙特利尔大学）；Helmholtz AI（亥姆霍兹人工智能研究所）
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
The University of Melbourne（墨尔本大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Peking University（北京大学）；Adelaide University（阿德莱德大学）；Hong Kong Baptist University（香港浸会大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Tsinghua University（清华大学）；Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS)（清华大学脑与认知科学研究所）；Chinese PLA General Hospital（中国人民解放军总医院）
Feedzai（Feedzai公司）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；DCC, Faculdade de Ciêncidas da Universidade do Porto（波尔图大学理学院数据与计算机科学系）；Instituto de Telecomunicações（电信研究所）
Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
Faculty of Computing（计算机学院）；Harbin Institute of Technology（哈尔滨工业大学）；The Artificial Intelligence Institute（人工智能研究所）；The Department of Computer Science（计算机科学系）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
