Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
Papers should be a three (3) page extended abstract, excluding references and appendices, and will be selected for contributed talks or for posters. Papers should be in PDF format and use the anonymized ACM Proceedings Template. Please submit your papers to the CMT web site.
If the research has previously appeared in a journal, workshop, or conference (including KDD 2021), the workshop submission should extend that previous work. Parallel submissions (such as to other conferences) are permitted.
Paper Submission Deadline: May 27th (extended), 2021, 11:59 PM Anywhere on Earth
Paper Notification: June 10th, 2021