  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.
