Learning to search efficiently for causally near-optimal treatments
Paper i 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

Göteborgs universitet

Viktor Lindblom

Student vid Chalmers

Omer Gottesman

Brown University

Fredrik Johansson

Chalmers, Data- och informationsteknik, Data Science

Advances in Neural Information Processing Systems

10495258 (ISSN)

Vol. 2020-December

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


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