Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；National Institute of Health Data Science, and Institute for Artificial Intelligence, Peking University（北京大学健康医疗大数据国家研究院、人工智能研究院）
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Northeast Georgia Health System（东北乔治亚医疗系统）；Palos Health（帕洛斯医疗中心）；Penn State University（宾夕法尼亚州立大学）；Purdue University（普渡大学）
Learning to Defer for Causal Discovery with Imperfect Experts
University of Oxford（牛津大学）；Mila – Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；ServiceNow Research（ServiceNow研究院）；University of Amsterdam（阿姆斯特丹大学）；Université Laval（拉瓦尔大学）
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）
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
ISR-Lisbon, Instituto Superior Técnico（里斯本信号与系统研究所，里斯本高等理工学院）；Digital Scholarship at Oxford, University of Oxford（牛津大学牛津数字学术中心）；Jet Propulsion Lab., Caltech（加州理工学院喷气推进实验室）
Getulio Vargas Foundation（热图利奥·瓦加斯基金会）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Münster（明斯特大学）；Aalto University（阿尔托大学）；University of Manchester（曼彻斯特大学）；University of São Paulo（圣保罗大学）；Basque Center for Applied Mathematics（巴斯克应用数学中心）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Institute of Medical Technology, Peking University Health Science Center（北京大学人民医院医学技术研究所）；National Institute of Health Data Science (NIHDS), Peking University（北京大学国家健康数据科学研究院）；Chalmers University of Technology（挑战大学）；NIHDS, Peking University（北京大学国家健康数据科学研究院）；Visualization Research Center (VISUS), University of Stuttgart（斯图加特大学可视化研究中心）；iDV x Lab, Tongji University（同济大学iDV x实验室）；National Institute of Traditional Chinese Medicine Constitution and Preventive Treatment of Diseases, Beijing University of Chinese Medicine（北京中医药大学国家中医体质与疾病预防治疗研究所）
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽人工智能学院）；Beijing Normal University（北京师范大学）；DataArc Tech Ltd.（数据弧科技有限公司）；Huawei Cloud BU（华为云业务单元）
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
Symbiosis Law School（symbiosis 法学院）；IISER Kolkata（印度科学教育与研究学院加尔各答分校）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Manipal University Jaipur（斋浦尔马尼帕尔大学）
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation
Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Indian Institute of Science Education and Research Kolkata（加尔各答印度科学教育与研究学院）；West Bengal National University of Juridical Sciences（西孟加拉国立法科大学）
Causal Graph based Event Reasoning using Semantic Relation Experts
Stony Brook University（石英 Brook 大学）；University of Pennsylvania（宾夕法尼亚大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；United States Naval Academy（美国海军学院）
Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
UIUC（伊利诺伊大学厄巴纳-香槟分校）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全研究中心）；MIT（麻省理工学院）；IIT Hyderabad（印度理工学院海得拉巴分校）；Microsoft Research（微软研究院）
