We invite researchers to submit work in (but not limited to) the following areas:

• Model-based RL with links to causal inference and counterfactual reasoning, e.g. causal bandits, off-policy evaluation and learning, counterfactual estimation from logged experiments, batch/offline learning from bandit feedback.
• Active causal learning, e.g. experiment design for learning causal structures or effects.
• Transportability, combining different experimental and observational data, especially in conjunction with transfer learning or RL settings.
• Causal transfer learning, applications of causal reasoning (e.g. invariances and conditional independences) to transfer learning.
• Estimation of (conditional) average treatment effects.

Submission Guidelines

Submissions of new ideas, recently published works and/or extension of existing works are welcome. Parallel submissions or submissions of under-review works are also permitted. The submission can either be a previously submitted conference or workshop paper, or an extended abstract of 1 to 3 pages (excluding references) in PDF format using NeurIPS style. Author names do not need to be anonymized.

Submissions will be accepted as poster presentations. The final versions will be posted on the workshop website (and are archival but do not constitute a proceeding).

Organizing committee

• Sara Magliacane (MIT-IBM Watson AI Lab, IBM Research)
• Karthikeyan Shanmugam (MIT-IBM Watson AI Lab, IBM Research)
• Kristjan Greenewald (MIT-IBM Watson AI Lab, IBM Research)
• Murat Kocaoglu (MIT-IBM Watson AI Lab, IBM Research)
• Dmitriy Katz-Rogozhnikov (MIT-IBM Watson AI Lab, IBM Research)

Key Dates

• Submission deadline: September 9, 2019.
• Notification date: September 10, 2019.
• Meeting Date: September 19, 2019.

Contact

All questions about submissions should be emailed to sara.magliacane@ibm.com.