Connectionists: [CFP] 4th Conference on Causal Learning and Reasoning (CLeaR 2025) - deadline Nov 2

[Sara Magliacane]

We invite submissions to the 4th 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.
Accepted papers will be published in the Proceedings of Machine Learning
Research (PMLR).
CLeaR 2025 (https://www.cclear.cc/2025) will be held in* Lausanne,
Switzerland*, *May 7 to May 9, 2025*.

Key information

   - *Paper submission deadline: Nov 2, 2024 11:59pm (Anywhere on Earth,
   AoE)*
   - Reviews released: Dec 13, 2024
   - Author rebuttals due: Dec 20, 2024 11:59pm (AoE)
   - Final decisions: Jan 27, 2025
   - Camera-ready deadline: Mar 9, 2025 11:59pm (AoE)
   - Conference dates and location: May 7 (Wed) - 9 (Fri), 2025 in
   Lausanne, Switzerland

Submit at https://openreview.net/group?id=cclear.cc/CLeaR/2025/Conference

The complete Call For Papers can be found at this link:
https://www.cclear.cc/2025/CallforPapers
For more information, check also the CLeaR website (
https://www.cclear.cc/2025) or follow our Twitter/X account (
https://x.com/Conf_CLeaR)

Summary

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. Paper Submission 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:

   - Foundational theories of causation
   - Causal effect identification and estimation
   - Causal discovery in complex environments
   - Efficient causal discovery in large-scale datasets
   - Causal representation learning
   - Machine learning (including reinforcement learning) building on causal
   principles
   - Unsupervised and semi-supervised deep learning connected to causality
   - Causal generative models for machine learning
   - Machine learning and statistical methods for heterogeneous data sources
   - Causality-empowered foundation models
   - Causally rooted methods for fairness, accountability, transparency,
   explainability, trustworthiness, and recourse
   - Benchmark for causal discovery and causal reasoning
   - Applications of any of the above to real-world problems

Physical Attendance

CLeaR 2025 is being planned as an in-person conference with hybrid elements
accommodating online presentations when physical attendance is not possible.
Formatting and Supplementary Material

Submissions are limited to 12 single-column PMLR-formatted pages, plus
unlimited additional pages for references and appendices. Authors of
accepted papers will have the option of opting out of the proceedings in
favor of a 1-page extended abstract, which will point to an open access
archival version of the full paper reviewed for CLeaR. You can also submit
a single file of additional supplementary material separately, which may be
either a pdf file (containing proof details, for instance) or a zip file
that can include multiple files of all formats (such as code or videos).
Note that reviewers are under no obligation to examine the supplementary
material. Please format the paper using the official LaTeX style files
<https://drive.google.com/drive/folders/1KTPiVeylR1_8o42kI8E2GbF-6ThMV6Zq>.
We do not support submission in formats other than LaTeX. Please do not
modify the layout given by the style file.


Mathias Drton & Biwei Huang
CLeaR 2025 Program Chairs

Negar Kiyavash & Jin Tian
CLeaR 2025 General Chairs
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