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Workshop
Tue Dec 14 10:50 AM -- 06:40 PM (PST)
Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice
Aurelien Bibaut · Maria Dimakopoulou · Nathan Kallus · Xinkun Nie · Masatoshi Uehara · Kelly Zhang





Workshop Home Page

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.

Opening Remarks
Closing Remarks (Remarks)
Understanding User Podcast Consumption Using Sequential Treatment Effect Estimation (Poster)
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects (Poster)
Deviation-Based Learning (Oral)
Doubly robust confidence sequences (Poster)
TALK (Razieh Nabi) (TALK)
Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning (Oral)
Partition-based Local Independence Discovery (Poster)
Estimating the Long-Term Effects of Novel Treatments (Poster)
MAGNET: Multi-Agent Graph Cooperative Bandits (Poster)
On Adaptivity and Confounding in Contextual Bandit Experiments (Poster)
Off-Policy Evaluation with Embedded Spaces (Poster)
What Would the Expert $do(\cdot)$?: Causal Imitation Learning (Poster)
Learning Treatment Effects in Panels with General Intervention Patterns (Poster)
TALK (Claire Vernade) (Talk)
Multiple imputation via state space model for missing data in non-stationary multivariate time series (Poster)
MAGNET: Multi-Agent Graph Cooperative Bandits (Oral)
What Would the Expert $do(\cdot)$?: Causal Imitation Learning (Oral)
Causal Multi-Agent Reinforcement Learning: Review and Open Problems (Poster)
TALK (Mark van der Laan) (Talk)
Deviation-Based Learning (Poster)
The Limits to Learning a Diffusion Model (Poster)
Beyond Ads: Sequential Decision-Making Algorithmsin Public Policy (Poster)
Dynamic Causal Discovery in Imitation Learning (Poster)
Chronological Causal Bandit (Poster)
Practical Policy Optimization with PersonalizedExperimentation (Poster)
TALK (Susan Athey) (Talk)
Bandits with Partially Observable Confounded Data (Poster)
Double/Debiased Machine Learning for Dynamic Treatment Effects via $g$-Estimation (Poster)
A Validation Tool for Designing Reinforcement Learning Environments (Poster)
Reinforcement Learning in Reward-Mixing MDPs (Poster)
A Causality-based Graphical Test to obtain an Optimal Blocking Set for Randomized Experiments (Poster)
TALK (Rui Song) (Talk)
TALK (Elias Bareibnboim) (TALK)
A Variational Information Bottleneck Principle for Recurrent Neural Networks (Poster)
Algorithms for Adaptive Experiments that Trade-offStatistical Analysis with Reward: CombiningUniform Random Assignment and RewardMaximization (Poster)
The Limits to Learning a Diffusion Model (Oral)
Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning (Poster)