Causal Structure
Causal structure research aims to uncover cause-and-effect relationships within complex systems, using both observational and interventional data to build accurate causal models. Current research focuses on developing robust algorithms for causal discovery, including those leveraging graph neural networks, score-matching techniques, and reinforcement learning to learn directed acyclic graphs (DAGs) efficiently, even in the presence of latent variables and non-linear relationships. These advancements are crucial for improving the reliability and interpretability of machine learning models across diverse fields, from healthcare and robotics to climate science and marketing, enabling more effective decision-making based on a deeper understanding of causality.
Papers
Everything that can be learned about a causal structure with latent variables by observational and interventional probing schemes
Marina Maciel Ansanelli, Elie Wolfe, Robert W. Spekkens
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu, Idan Attias, Daniel M. Roy
Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models
Carlos Fernández-Loría, Yanfang Hou, Foster Provost, Jennifer Hill
Investigating potential causes of Sepsis with Bayesian network structure learning
Bruno Petrungaro, Neville K. Kitson, Anthony C. Constantinou
Introducing Diminutive Causal Structure into Graph Representation Learning
Hang Gao, Peng Qiao, Yifan Jin, Fengge Wu, Jiangmeng Li, Changwen Zheng