Call for Papers

The UAI Causal workshop welcomes contributions from a variety of perspectives from machine learning, statistics, economics, philosophy, and social sciences, among others. We expect the workshop to cover an array of the latest methodological and applied research in causal inference. This includes, but is not limited to, the following topics:


  • causal learning methods, including novel ways of exploiting expert knowledge,

  • methods for causal prediction and causal effect evaluation,

  • methods evaluating the quality of causal prediction,

  • methods addressing causal miss-specification and hidden confounders,

  • addressing the challenge of practical causal inference in the context of real-world applications.


At the discretion of the organizers, some contributions will be assigned slots as short contributed talks and others will be presented as posters.

Submission instructions and deadline

31st of May, 23:59 UTC time. Submit your extended abstracts here: https://cmt3.research.microsoft.com/CASUALUAI21

We suggest extended abstracts of 2 pages in the UAI format, but no specific format is enforced. A maximum of 4 pages without references will be considered. Please anonymize the author names. PDF files only.

For further questions, please contact Ema Perković (perkovic [at] uw [.] edu).

Notification

We will notify you of acceptance by June 30th, 2021.

Note: There will be no formal proceedings for this workshop. Therefore, we welcome submissions of abstracts of papers under review. We will post the accepted extended abstracts on this webpage (similar to the 2018 UAI causal workshop).

Note 2: If your paper on causal inference has been accepted at the main UAI conference and you want to be a part of the workshop as well, please contact: Ema Perković (perkovic [at] uw [.] edu).