Cause Effect

Cause-effect inference aims to uncover the true causal relationships between variables, moving beyond simple correlations. Current research focuses on developing robust algorithms for causal discovery, particularly in high-dimensional and complex systems, employing techniques like constraint-based methods, score-based methods, and those leveraging large language models and structural equation models to analyze diverse data types (including text and time series). These advancements are crucial for improving the explainability and trustworthiness of AI systems, enhancing scientific understanding in various fields (e.g., medicine, climate science), and enabling more effective decision-making in complex real-world scenarios.