Causal Graph
Causal graphs are probabilistic graphical models used to represent causal relationships between variables, aiming to infer cause-and-effect structures from observational or interventional data. Current research focuses on developing algorithms for causal discovery, including constraint-based and score-based methods, often incorporating techniques like Bayesian inference, neural networks (e.g., convolutional neural networks), and reinforcement learning to improve scalability and accuracy, especially in high-dimensional datasets with missing data or latent confounders. These advancements have significant implications for various fields, enabling more robust and explainable AI systems, improved decision-making in complex systems (e.g., supply chains, healthcare), and deeper understanding of biological processes.
Papers
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Nengbo Wang, Xiaotian Han, Jagdip Singh, Jing Ma, Vipin ChaudharyCase Western Reserve UniversityCausal Bayesian Optimization with Unknown Graphs
Jean Durand, Yashas Annadani, Stefan Bauer, Sonali ParbhooImperial College London●Technical University of Munich (TUM)
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
Khadija Zanna, Akane SanoRice UniversityUnitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery
Rebecca J. Herman, Jonas Wahl, Urmi Ninad, Jakob RungeCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)●German Aerospace Center (DLR)●German Research Centre for Artificial...+2
R2: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs
Zefeng Lin, Yi Xiao, Zhiqiang Mo, Qifan Zhang, Jie Wang, Jiayang Chen, Jiajing Zhang, Hui Zhang, Zhengyi Liu, Xianyong Fang, Xiaohua XuUniversity of Science and Technology of China●Anhui Jianzhu University●Anhui UniversityDeCaFlow: A Deconfounding Causal Generative Model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras, Santiago Zazo, Isabel ValeraUniversidad Polit ´ecnica de Madrid●University of Edinburgh●Saarland University●Max Planck Institute for Software Systems
Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities
Tao Feng, Yunke Zhang, Huandong Wang, Yong LiTsinghua UniversityCausal Discovery and Inference towards Urban Elements and Associated Factors
Tao Feng, Yunke Zhang, Xiaochen Fan, Huandong Wang, Yong LiTsinghua University●Institute for Electronics and Information Technology in Tianjin
Multi-Objective Causal Bayesian Optimization
Shriya Bhatija, Paul-David Zuercher, Jakob Thumm, Thomas BohnéTechnical University of Munich●University of Cambridge●The Alan Turing InstituteInternal Incoherency Scores for Constraint-based Causal Discovery Algorithms
Sofia Faltenbacher, Jonas Wahl, Rebecca Herman, Jakob RungeTechnical University Dresden●German Research Center for Artificial Intelligence (DFKI)