Causal Relationship
Causal relationship research aims to understand and model cause-and-effect relationships within complex systems, moving beyond simple correlations. Current research focuses on developing algorithms and models, such as Bayesian networks, that can effectively learn causal structures from observational and interventional data, often incorporating expert knowledge or handling high-dimensional and incomplete datasets. These advancements are crucial for improving decision-making in various fields, including healthcare, finance, and robotics, by enabling more accurate predictions and interventions based on a deeper understanding of underlying causal mechanisms. The development of robust benchmarking frameworks further enhances the reliability and reproducibility of causal discovery methods.
Papers
Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
Ahmet Kapkiç, Pratanu Mandal, Shu Wan, Paras Sheth, Abhinav Gorantla, Yoonhyuk Choi, Huan Liu, K. Selçuk Candan
Influence of Backdoor Paths on Causal Link Prediction
Utkarshani Jaimini, Cory Henson, Amit Sheth
Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Infer Causal Links Between Siamese Images
Zhiyuan Li, Heng Wang, Dongnan Liu, Chaoyi Zhang, Ao Ma, Jieting Long, Weidong Cai
Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks
Rujia Shen, Boran Wang, Chao Zhao, Yi Guan, Jingchi Jiang