Connectionists: 1st ever conference on Causal Learning and Reasoning conference (CLearR)

[Nan Rosemary Ke]

We are excited to announce the call for papers for the first ever *causal
learning and reasoning Conference *CLeaR <https://www.cclear.cc/2022>.

The conference is aimed at work in causal learning and  the intersection of
causal learning and machine learning. The submission deadline is: *Oct
22nd, 2021*, we look forward to seeing your work at the conference :)  See
below for details.

========= CLeaR conference =============

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.

We invite submissions to the *1st 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.

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.


Rosemary Ke,
on behalf of all CLeaR organizers


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