Learning to search efficiently for causally near-optimal treatments
Paper in proceeding, 2020

Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The methods are evaluated on synthetic and real-world healthcare data and compared to model-free reinforcement learning. We find that our methods compare favorably to the model-free baseline while offering a more transparent trade-off between search time and treatment efficacy.


Samuel Håkansson

University of Gothenburg

Viktor Lindblom

Student at Chalmers

Omer Gottesman

Brown University

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Advances in Neural Information Processing Systems

10495258 (ISSN)

Vol. 2020-December

34th Conference on Neural Information Processing Systems, NeurIPS 2020
Virtual, Online, ,

Subject Categories

Probability Theory and Statistics

Computer Science

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1/3/2024 9