CLear 2022: Call for Papers

Attention conservation notice: An invitation to put a lot of effort into writing about a recondite academic topic, only to have it misunderstood by anonymous strangers.

Having agreed to be an area chair (area TBD), I ought to publicize the call for papers for the first Conference on Causal Learning and Reasoning (CLeaR 2022):

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 facilitate formulating, understanding, and tackling a broad range of problems, including domain generalization, robustness, trustworthiness, and fairness across machine learning, reinforcement learning, and statistics.

We invite papers 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.

The proceedings track is the standard CLeaR paper submission track. Papers will be selected via a rigorous double-blind peer-review process. All accepted papers will be presented at the Conference as contributed talks or as posters and will be published in the Proceedings.

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

The deadline is 22 October 2021; further details are available at the conference website.

(I should write up my "Apology for Causal Discovery" as a proper paper or at least essay, rather than a pair of slide decks and a video which [like all recordings of me] I can't stand to watch, but that's so far back in the queue I could cry.)

Constant Conjunction Necessary Connexion; Kith and Kin