Important dates:

• Submission deadline: October 7th, 2021 (AOE) September 30th, 2021 (AoE)

  • Submissions accepted via CMT

• Workshop: December 14th, 2021

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, biostatistics/healthcare, economics/evidence-based policymaking, applications in the industry such as online advertising, and ethics/law.

The primary purpose of this workshop is to convene both experts, practitioners, and interested 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.

Some topics that the workshop will engage with include:

• Dynamic treatment regimes

• Causal inference and discovery from longitudinal and panel data

• Unmeasured confounding in sequential decisions and sensitivity analyses

• Causality in dynamical systems

• Econometric/structural estimation in sequential settings

• Off-policy evaluation and logged bandits

• Offline reinforcement learning and imitation learning

• A/B-testing and design of experiments

• Algorithmic fairness in dynamic environments

• Causal graphs with multiple and/or sequential interventions

• Online allocation and online linear programs

• Data-driven inverse optimization in sequential settings

• Applications, including in healthcare, e-commerce, and policymaking

Organizing Team:

• Aurelien Bibaut, Senior Research Scientist at Netflix Research

• Maria Dimakopoulou, Senior Research Scientist at Netflix Research

• Nathan Kallus, Assistant Professor in the School of Operations Research and Information Engineering at Cornell University & Senior Research Scientist at Netflix Research

• Xinkun Nie, Ph.D. Candidate in the Department of Computer Science at Stanford University

• Masatoshi Uehara, Ph.D. Candidate in the Department of Computer Science at Cornell University

• Kelly Zhang, Ph.D. Candidate in the Department of Computer Science at Harvard University

Programme Committee Members

Will be added soon