TH19: Foundations and Practical Applications in Causal Decision Making
Wednesday, February 21, 2:00 pm - 6:00 pm
AAAI 2024 - Vancouver, Canada
Wednesday, February 21, 2:00 pm - 6:00 pm
AAAI 2024 - Vancouver, Canada
To make effective decisions, it’s important to have a thorough understanding of the causal connections among actions, environments, and outcomes. This tutorial aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision-making via causal policy learning. This tutorial aims to offer a comprehensive methodology and practical implementation framework by consolidating various methods in this area into a Python-based collection. This tutorial will provide a unified framework for you to understand areas including causal inference, causal discovery, randomized experiments, dynamic treatment regimes, bandits, reinforcement learning, and so on. This tutorial is based on an online book with an accompanying Python package (in progress; collaboration is welcomed)
Introduction and Overview on Causal Decision Making (Rui Song): 2:00 pm - 2:50 pm
Break: 2:50 pm - 2:55 pm
Causal Structure Learning (Hengrui Cai): 2:55 pm - 3:30 pm
Break: 3:30 pm - 4:00 pm
Causal Effect Learning (Yang Xu): 4:00 pm - 4:35 pm
Causal Policy Learning - offline (Runzhe Wan): 4:35 pm - 5:10 pm
Causal Structure Learning - online (Lin Ge): 5:10 pm - 5:45 pm
Floor Discussion: 5:45 pm - 6:00 pm
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