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

Our method is used for the data-efficient evaluation of the regret of past treatment assignments. Unlike approaches such as A/B testing, our method avoids assigning treatments that are known to be highly sub-optimal, whilst engaging in some exploration to gather pertinent information. We achieve this by introducing an information-based design objective, which we optimise end-to-end.

Implicit Deep Adaptive Design: Policy–Based Experimental Design without Likelihoods

iDAD allows us to practically run Bayesian optimal experiments with implicit (likelihood-free) models in real-time. Previous methods either relied on an explicit likelihood model of the outcomes, or were too computationally costly to run in real-time.

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

DAD is the first policy-based approach to BOED that enables adaptive experiments to be performed in real-time.