麻豆村

Welcome to the 麻豆村-CLeaR Group! CLeaR (Causal Learning and Reasoning) is a research group led by Professors , Peter Spirtes, Clark Glymour, and Joseph Ramsey at 麻豆村.

Research synopsis: Our research interests lie in machine learning and artificial intelligence, especially in causal discovery, causal representation learning, and causality-related ML and AI. On the causal learning side, we develop methods for automated causal discovery or representation learning from various kinds of data. Our research has delivered state-of-the-art causal discovery methodological developments by considering many of the challenges to causal learning, including latent confounding, distribution shifts, nonlinear relationships, general data distributions, and selection bias. On the ML/AI side, we investigate learning problems including transfer learning, concept learning, reinforcement learning, and deep learning from a causal view. On the philosophy side, we study philosophical foundations of causation and various machine learning tasks, and explore ethical issues of artificial intelligence. On the application side, we develop domain-specific causality algorithms to solve exciting real-world applications in neuroscience, biology, healthcare, computer vision, computational finance, and climate analysis.