This workshop aims to highlight the latest advancements and explore future directions in causal inference, an area of increasing importance in artificial intelligence. Causal inference enables researchers and practitioners to understand and model cause-effect relationships beyond mere correlations, offering profound implications for data science, machine learning, and AI. Given UAI's focus on the uncertainty aspects of AI, incorporating causal reasoning and inference provides essential insights into building more robust, interpretable, and fair AI decision systems. This workshop will cover a broad spectrum of topics, including but not limited to, causal discovery, causal effect estimation, counterfactual reasoning, and applications of causal inference in machine learning and AI.

The call for abstract is closed. 

Organizing Committee