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arXiv · 2007.00973

Learning to search efficiently for causally near-optimal treatments

Samuel Håkansson·Viktor Lindblom·Omer Gottesman·Fredrik D. Johansson
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Abstract

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.

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Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson. 2021-02-17. Learning to search efficiently for causally near-optimal treatments. https://arxiv.org/abs/2007.00973

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