1 research outputs found
Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
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
singlemindedly 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. The key here is that a low-cost surrogate function can be an accurate
proxy for the very computationally costly optimal tree in terms of expected
regret. This surrogate can be fit many times with different hyperparameter
values to proxy the performance of the optimal model. The method is applied to
a real-world case-study of conditional cash transfers in Morocco where hybrid
(partially optimal, partially greedy) policy trees provide good performance as
a surrogate for optimal trees while being computationally cheap enough to
feasibly fit a Pareto frontier.Comment: 15 pages, 6 figure