Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation

Patrick Rehill () and Nicholas Biddle ()
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Patrick Rehill: Australian National University
Nicholas Biddle: Australian National University

Computational Economics, 2025, vol. 66, issue 2, No 1, 1001 pages

Abstract: 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 method is applied to a real-world case-study of pricing targetting subsididies for anti-malarial medication in Kenya.

Keywords: Policy learning; Multi-objective Bayesian optimisation; Optimal decision trees; Heterogeneous treatment effects; Data-driven decision making (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10722-1

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