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We invite participation in NIPS 2017 Workshop:

“From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making” -- December 8th  2017,  Long Beach, USA.  





Leon Bottou (Facebook, US)

Emma Brunskill (CMU)
Guido Imbens (Stanford)

Jasjeet Sekhon (UC Berkeley)

David Sontag (MIT)
Matt Taddy (Microsoft)


In recent years machine learning and causal inference have both seen important advances, especially through a dramatic expansion of their theoretical and practical domains. This workshop is aimed at facilitating more interactions between researchers in machine learning, causal inference, and application domains that use both for intelligent decision making. To this effect, the 2017 'What If?' To 'What Next?' 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:

-          Combining experimental control and observational data

-          Bandit algorithms and reinforcement learning with explicit links to causal inference and counterfactual reasoning

-          Interfaces of agent-based systems and causal inference

-          Handling selection bias

-          Large-scale algorithms

-          Applications in online systems (e.g. search, recommendation, ad placement)

-          Applications in complex systems (e.g. cell biology, smart cities, computational social sciences)

-          Interactive experimental control vs. counterfactual estimation from logged experiments

-          Discriminative learning vs. generative modeling in counterfactual settings

We invite contributions both in the form of extended abstract and full papers. At the discretion of the organizers, some contributions will be assigned slots as short contributed talks and others will be presented as posters.

Submission Instructions:

Submission website

Submission length: 2 page extended abstracts or up to 8 page full paper
Please use the NIPS 2017 submission format

Please note that at least one author of each accepted paper must be available to present the paper at the workshop.

Organizing Committee:

Thorsten Joachims, Information Science and Computer Science, Cornell University

Nathan Kallus, Cornell Tech and ORIE, Cornell University

Lihong Li, Deep Learning Technology Center, Microsoft Research

Adith Swaminathan, Deep Learning Technology Center, Microsoft Research

Ricardo Silva, Statistical Science, UCL

John S Shawe-Taylor, Computer Science, UCL

Panos Toulis, Econometrics and Statistics, Booth School of Business, University of Chicago

Alexander Volfovsky, Statistical Science, Duke University


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