AAAI’25 Workshop on Artificial Intelligence with Causal Techniques

Causality aims to describe the principle that certain events cause specific outcomes, helping us understand, predict, and explain changes in the world. Recently, the connection between causality and AI has become increasingly important, where AI can benefit from causal reasoning to build more robust, interpretable, and generalizable models. Therefore, people seek to use AI with causal techniques to benefit various communities like healthcare, e-commerce, and social science.

The Artificial Intelligence with Causal Technologies (AICT) workshop aims to discuss recent advances in causal methodology, including novel causal discovery and causal inference methods, as well as methods for downstream causal tasks such as causal representation learning, causal reinforcement learning, causal fairness, etc. We will also explore how these advances in the causal community can contribute to different subfields of AI such as recommender systems, natural language processing, computer vision, etc. In addition, it is interesting to discuss the intersection of causality and large models, including how large models can be utilized to improve the performance of causal tasks, as well as how causal insights can be used to enhance the reasoning ability and reliability of large models.