BCIRWIS 2021: Bayesian causal inference for real world interactive systems - (KDD 2021 Workshop)
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:

* Offline evaluation of recommender and interactive systems.
* Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
* Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
* Probabilistic approaches to incrementality and attribution.
* Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
* Bayesian methods in a production environment.

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 10th, 2021, 11:59 PM Anywhere on Earth

Paper Notification: June 10th, 2021