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.

We invite submissions to the 2nd conference on Causal Learning and Reasoning (CLeaR), and welcome paper submissions that describe new theory, methodology, and/or applications relevant to any aspect of causal learning and reasoning in the fields of artificial intelligence and statistics. Submitted papers will be evaluated based on their novelty, technical quality, and potential impact. Experimental methods and results are expected to be reproducible, and authors are strongly encouraged to make code and data available. We also encourage submissions of proof-of-concept research that puts forward novel ideas and demonstrates potential for addressing problems at the intersection of causality and machine learning. CLeaR 2023 will be held in Tübingen, Germany from April 11 to 14, 2023, with virtual elements.
Topics of submission may include, but are not limited to:

Machine learning building on causal principles
Causal discovery in complex environments
Efficient causal discovery in large-scale datasets
Causal effect identification and estimation
Causal generative models for machine learning
Unsupervised and semi-supervised deep learning connected to causality
Machine learning with heterogeneous data sources
Benchmark for causal discovery and causal reasoning
Reinforcement learning
Fairness, accountability, transparency, explainability, trustworthiness, and recourse
Applications of any of the above to real-world problems
Foundational theories of causation

Submit at https://openreview.net/group?id=cclear.cc/CLeaR/2023/Conference.


Important Dates
Paper submission Oct 28, 2022 11:59pm (Anywhere on Earth, AoE)
Reviews released Dec 2, 2022
Author rebuttals due Dec 9, 2022 11:59pm (AoE)
Final decisions Jan 12, 2023
Camera ready deadline Feb 20, 2023 11:59pm (AoE)
Main conference Apr 11 - Apr 14 2023