NeurIPS 2019 Workshop

“Do the right thing”: machine learning and causal inference for improved decision making

December 13 – 14, 2019, Vancouver

Workshop Summary

In recent years, machine learning has seen important advances in its theoretical and practical domains, with some of the most significant applications in online marketing and commerce, personalized medicine, and data-driven policy-making. This dramatic success has led to increased expectations for autonomous systems to make the right decision at the right target at the right time.  This gives rise to one of the major challenges of machine learning today that is the understanding of the cause-effect connection. Indeed, actions, intervention, and decisions have important consequences, and so, in seeking to make the best decision, one must understand the process of identifying causality. By embracing causal reasoning autonomous systems will be able to answer counterfactual questions, such as “What if I had treated a patient differently?”, and “What if had ranked a list differently?” thus helping to establish the evidence base for important decision-making processes. 

The purpose of this workshop is to bring together experts from different fields to discuss the relationships between machine learning and causal inference and to discuss and highlight the formalization and algorithmization of causality toward achieving human-level machine intelligence.

Invited Speakers

Susan Athey

Stanford University

Susan Murphy

Harvard University

Andrea Rotnitzky

Harvard University and Universidad di Tella

Susan Shortreed

Kaiser Permanente Washington Health Research Institute

Ying-Qi Zhao

University of Washington and the Fred Hutchinson Cancer Research Center

Call for Papers

We solicit submissions 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
  • Reinforcement Learning and Causal Inference
  • Causal transfer learning
  • Mediation analysis
  • 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)

We suggest extended abstracts of 2 pages in the NeurIPS format, but no specific format is enforced. The review process is double-blind. 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.

Deadline for Submission: September 9, 2019; Acceptance Decision: October 1, 2019


Thorsten Joachims

Cornell University

Nathan Kallus

Cornell University

Adith Swaminathan

Microsoft Research

Michele Santacatterina

Cornell University

David Sontag


Angela Zhou

Cornell University

Program Schedule