Program Highlights »
Workshop
Fri Dec 8th 08:30 AM -- 06:30 PM @ Hall C
From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making
Ricardo Silva · Panagiotis Toulis · John S Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan





Workshop Home Page

In recent years machine learning and causal inference have both seen important advances, especially through a dramatic expansion of their theoretical and practical domains. Machine learning has focused on ultra high-dimensional models and scalable stochastic algorithms, whereas causal inference has been guiding policy in complex domains involving economics, social and health sciences, and business. Through such advances a powerful cross-pollination has emerged as a new set of methodologies promising to deliver robust data analysis than each field could individually -- some examples include concepts such as doubly-robust methods, targeted learning, double machine learning, causal trees, all of which have recently been introduced.

This workshop is aimed at facilitating more interactions between researchers in machine learning and causal inference. In particular, it is an opportunity to bring together highly technical individuals who are strongly motivated by the practical importance and real-world impact of their work. Cultivating such interactions will lead to the development of theory, methodology, and - most importantly - practical tools, that better target causal questions across different domains.

In particular, we will highlight theory, algorithms and applications on automatic decision making systems, such as recommendation engines, medical decision systems and self-driving cars, as both producers and users of data. The challenge here is the feedback between learning from data and then taking actions that may affect what data will be made available for future learning. Learning algorithms have to reason about how changes to the system will affect future data, giving rise to challenging counterfactual and causal reasoning issues that the learning algorithm has to account for. Modern and scalable policy learning algorithms also require operating with non-experimental data, such as logged user interaction data where users click ads suggested by recommender systems trained on historical user clicks.

To further bring the community together around the use of such interaction data, this workshop will host a Kaggle challenge problem based on the first real-world dataset of logged contextual bandit feedback with non-uniform action-selection propensities. The dataset consists of several gigabytes of data from an ad placement system, which we have processed into multiple well-defined learning problems of increasing complexity, feedback signal, and context. Participants in the challenge problem will be able to discuss their results at the workshop.

08:30 AM Introductions (Panel)
Panos Toulis, Alexander Volfovsky
08:45 AM Looking for a Missing Signal (Invited Talk)
Leon Bottou
09:20 AM Invited Talk
Emma Brunskill
10:00 AM Contributed Talk 1 (Contributed Talks)
David Heckerman
10:15 AM Contributed Talk 2 (Contributed Talks)
Jovana Mitrovic
11:00 AM Invited Talk 3 (Invited Talk)
Richard Hahn
11:35 AM Invited Talk 4 (Invited Talk)
David Sontag
12:10 PM Poster session
Abbas Zaidi, Christoph Kurz, David Heckerman, YiJyun Lin, Stefan Riezler, Ilya Shpitser, Songbai Yan, Olivier Goudet, Yash Deshpande, Judea Pearl, Jovana Mitrovic, Brian Vegetabile, Tae Hwy Lee, Karen Sachs, Karthika Mohan, Reagan Rose, Julius Ramakers, Negar Hassanpour, Pierre Baldi, Razieh Nabi, Noah Hammarlund, Eli Sherman, Carolin Lawrence, Fattaneh Jabbari, Vira Semenova, Maria Dimakopoulou, Pratik Gajane, Russell Greiner, Ilias Zadik, Alex Blocker, Hao Xu, Tal EL HAY, Tony Jebara, Benoit Rostykus
01:35 PM Contributed Talk 3 (Contributed Talks)
Ilya Shpitser
01:50 PM Contributed Talk 4 (Contributed Talks)
Judea Pearl
02:05 PM Invited Talk 5 (Invited Talk)
guido imbens
03:30 PM Invited Talk
Bin Yu
04:05 PM Causal inference with machine learning (Discussion Panel)
05:00 PM Causality and Machine Learning Challenge: Criteo Ad Placement Challenge (Posters, discussion and talks)