Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation

• URL: http://arxiv.org/abs/2212.06312v2
• Date: Tue, 17 Oct 2023 05:37:29 GMT
• Title: Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
• Authors: Patrick Rehill and Nicholas Biddle
• Abstract summary: Multi-Objective Policy Learning combines optimal decision trees for policy learning with a multi-objective Bayesian optimisation approach. The method is applied to a real-world case-study of non-price rationing of anti-malarial medication in Kenya.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making contexts, decision-makers often care about trade-offs between outcomes, not just single-mindedly maximising utility for one outcome. This paper proposes an approach termed Multi-Objective Policy Learning (MOPoL) which combines optimal decision trees for policy learning with a multi-objective Bayesian optimisation approach to explore the trade-off between multiple outcomes. It does this by building a Pareto frontier of non-dominated models for different hyperparameter settings which govern outcome weighting. The key here is that a low-cost greedy tree can be an accurate proxy for the very computationally costly optimal tree for the purposes of making decisions which means models can be repeatedly fit to learn a Pareto frontier. The method is applied to a real-world case-study of non-price rationing of anti-malarial medication in Kenya.

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