ICML / IJCAI / AAMAS 2018
Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML)

July 15, 2018
Stockholm, Sweden

Many of the most impactful applications of machine learning are not just about prediction, but are about putting learning systems in control of selecting the right action at the right time. Examples of such systems range from search engines that act by displaying a ranking, to medical decision support systems, recommender systems, ad placement systems, conversational systems, automated trading platforms, computer games, and cyber-physical systems like self-driving cars. This focus on acting requires some causal understanding of the world, since actions are interventions that change the distribution of data unlike in standard prediction problems. This gives rise to challenging counterfactual and causal prediction problems. However, causality is only a means to an end - namely being able to take the right actions; one typically does not have the burden of providing strong proofs of causal discovery.

We solicit submission of novel research related to all aspects of causal inference, counterfactual prediction, and autonomous action. This includes, but is not limited to, the following topics:

- Predicting counterfactual outcomes
- Estimation of (conditional) average treatment effects
- Contextual bandit algorithms and on-policy learning
- Batch/offline learning from bandit feedback
- Off-policy evaluation and learning
- Interactive experimental control vs. counterfactual estimation from logged experiments
- Online A/B-testing vs. offline A/B-testing
- De-biasing observational data and feedback cycles
- Fairness of actions and causal aspects of fairness
- Applications in online systems (e.g. search, recommendation, ad placement)
- Applications in physical systems (e.g. cars, smart homes)
- Applications in medicine (e.g. personalized treatment, clinical trials)

Confirmed invited speakers: Alekh Agarwal, Victor Chernozhukov, Alexandra Chouldechova, Mohammad Ghavamzadeh, Jonas Peters, Don Rubin, Suchi Saria, Csaba Szepesvari, Stefan Wager

We suggest extended abstracts of 2 pages in ICML format, but no specific format is enforced. A maximum of 8 pages will be considered. References will not count towards the page limit. PDF files only. At the discretion of the organizers, accepted contributions will be assigned slots as contributed talks and others will be presented as posters. The deadline for submissions is

(UPDATED) May 23, 2018 via https://sites.google.com/site/faim18wscausalml/

As part of the workshop, we are organizing a CrowdAI competition on learning from logged contextual bandit feedback with non-uniform action-selection propensities. The data is provided by Criteo, and a separate announcement is forthcoming. The winners will be invited to present their approach at the workshop.

Adith Swaminathan (Microsoft Research)
Clement Calauzenes (Criteo)
Nathan Kallus (Cornell)
Philip Thomas (UMass Amherst)
Thorsten Joachims (Cornell)