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The real-world testing of decisions made using causal machine learning modelsis an essential prerequisite for their successful application. We focus onevaluating and improving contextual treatment assignment decisions: these arepersonalised treatments applied to e.g. customers, each with their owncontextual information, with the aim of maximising a reward. In this paper weintroduce a model-agnostic framework for gathering data to evaluate and improvecontextual decision making through Bayesian Experimental Design. Specifically,our method is used for the data-efficient evaluation of the regret of pasttreatment assignments. Unlike approaches such as A/B testing, our method avoidsassigning treatments that are known to be highly sub-optimal, whilst engagingin some exploration to gather pertinent information. We achieve this byintroducing an information-based design objective, which we optimiseend-to-end. Our method applies to discrete and continuous treatments. Comparingour information-theoretic approach to baselines in several simulation studiesdemonstrates the superior performance of our proposed approach.

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation