Active learning for optimal intervention design in causal models

Active learning for optimal intervention design in causal models

Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When the space of possible interventions is large, making an exhaustive search infeasible, experimental design strategies are...

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Bibliographic Details
Main Authors: Zhang, Jiaqi, Cammarata, Louis, Squires, Chandler, Sapsis, Themistoklis P., Uhler, Caroline
Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Format: Article
Language:English
Published: Springer Science and Business Media LLC 2024
Subjects:
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Human-Computer Interaction
Software
Online Access:https://hdl.handle.net/1721.1/154216

Internet

https://hdl.handle.net/1721.1/154216

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