Call for papers

The UAI Causal workshop welcomes contributions from a variety of perspectives from machine learning, statistics, economics and social sciences, among others. This includes, but it is not limited to, the following topics:

• Causal discovery and relational learning
• Estimation of causal graphs and related tasks
• Measures and methods for evaluating the quality of causal predictions
• Combining experimental control and observational data
• Interactive experimental control vs. counterfactual estimation from logged experiments
• Bandit algorithms and reinforcement learning
• Discriminative learning vs. generative modeling in counterfactual settings
• Handling selection bias
• Identification and estimating causal effects from observational data
• Deriving testable implications of causal models
• Applications in online systems (e.g. search, recommendation, ad placement)
• Applications in complex systems (e.g. cell biology, smart cities, computational social sciences)

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

Format

We suggest extended abstracts of 2 pages in the UAI format, but no specific format is enforced. A maximum of 6 pages will be considered. PDF files only.

Submission page: https://easychair.org/conferences/?conf=causaluai2018

Submission instructions and deadline

20th of May, 23:59 GMT time. Please submit your PDF via.

For further questions, please contact Bryant Chen, Alexander Volfovsky or Panos Toulis.

Notification

We will notify acceptance by 20th of June, 2018.