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Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice
Aurelien Bibaut · Maria Dimakopoulou · Nathan Kallus · Xinkun Nie · Masatoshi Uehara · Kelly Zhang

Tue Dec 14 10:50 AM -- 07:30 PM (PST) @ None
Event URL: https://sites.google.com/view/causal-sequential-decisions/home »

Sequential decision-making problems appear in settings as varied as healthcare, e-commerce, operations management, and policymaking, and depending on the context these can have very varied features that make each problem unique. Problems can involve online learning or offline data, known cost structures or unknown counterfactuals, continuous actions with or without constraints or finite or combinatorial actions, stationary environments or environments with dynamic agents, utilitarian considerations or fairness or equity considerations. More and more, causal inference and discovery and adjacent statistical theories have come to bear on such problems, from the early work on longitudinal causal inference from the last millenium up to recent developments in bandit algorithms and inference, dynamic treatment regimes, both online and offline reinforcement learning, interventions in general causal graphs and discovery thereof, and more. While the interaction between these theories has grown, expertise is spread across many different disciplines, including CS/ML, (bio)statistics, econometrics, ethics/law, and operations research.

The primary purpose of this workshop is to convene both experts, practitioners, and interested young researchers from a wide range of backgrounds to discuss recent developments around causal inference in sequential decision making and the avenues forward on the topic, especially ones that bring together ideas from different fields. The all-virtual nature of this year's NeurIPS workshop makes it particularly felicitous to such an assembly. The workshop will combine invited talks and panels by a diverse group of researchers and practitioners from both academia and industry together with contributed talks and town-hall Q\&A that will particularly seek to draw from younger individuals new to the area.

Author Information

Aurelien Bibaut (Netflix)
Maria Dimakopoulou (Stanford University)
Nathan Kallus (Cornell University)
Xinkun Nie (Stanford University)
Masatoshi Uehara (Cornell University)
Kelly Zhang (Harvard University)

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