Causal Discovery
Causal discovery aims to infer cause-and-effect relationships from data, moving beyond simple correlations to understand underlying mechanisms. Current research emphasizes developing algorithms and models, including constraint-based methods, score-matching techniques, and those leveraging neural networks (like graph neural networks and normalizing flows), to efficiently handle high-dimensional data, time series, and latent variables, often incorporating expert knowledge or interventional data to improve accuracy. This field is crucial for advancing scientific understanding across diverse domains, from biology and healthcare to climate science and robotics, by enabling more accurate modeling, prediction, and intervention design. Improved causal discovery methods are leading to more reliable insights and more effective decision-making in complex systems.
Papers
Discovering maximally consistent distribution of causal tournaments with Large Language Models
Federico Baldo, Simon Ferreira, Charles K. Assaad
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa
Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo, Dongkuan Xu, Haifeng Chen, Jingchao Ni
Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective
Zhenyu Wang, Yifan Hu, Peter Bühlmann, Zijian Guo
Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
Mulugeta Weldezgina Asres, Christian Walter Omlin, The CMS-HCAL Collaboration