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.