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

TL;DR

This paper introduces Multi-Objective Policy Learning (MOPoL), a method combining optimal decision trees with multi-objective Bayesian optimisation to effectively explore trade-offs between multiple policy outcomes, demonstrated on a healthcare case study.

Contribution

It presents a novel approach that efficiently models trade-offs in policy outcomes using Pareto frontiers and surrogate models, enhancing multi-objective policy decision-making.

Findings

Successfully applied to anti-malarial medication rationing in Kenya

Efficiently approximates optimal trees with low-cost greedy trees

Provides a Pareto frontier of trade-offs between outcomes

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.