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Causal AI & LLM synergies: Enterprise decision making needs more than chatbots

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We are excited to sponsor and attend CLeaR 2023!

Causality is a fundamental notion in science and engineering. In the past few decades, some of the most influential developments in the study of causal discovery, causal inference, and the causal treatment of machine learning have resulted from cross-disciplinary efforts. In particular, a number of machine learning and statistical analysis techniques have been developed to tackle classical causal discovery and inference problems. On the other hand, the causal view has been shown to be able to facilitate formulating, understanding, and tackling a number of hard machine learning problems in transfer learning, reinforcement learning, and deep learning.

CLeaR 2022: Starting a brand new conference in these pandemic years and ensuring it is set up for long-term success has been a significant undertaking. Despite these challenges, more than 50 people attended the conference in person and several hundred connected remotely. We had 9 oral presentations and 40 posters, covering topics that range from causal discovery, causal fairness, explainability, non-parametric inference, causal Markov decision processes, to social-influence estimation, applications of causality, and other topics. We have received a number of enquiries about whether and where to hold CLeaR 2023 and are delighted to announce the next edition.