Causal Elicitation for Bayesian Optimization

Jakob Zeitler, Raul Astudillo

Keywords: causal inference, bayesian optimisation, preference elicitation

Abstract:

Causal Inference allows scientists and businesses to draw causal conclusions about e.g. their drug- development or marketing campaign.

Causal Entropy Search Branchini et al. [2023] was introduced as a way to learn both the causal graph as well as optimise an intervention of interest at the same time. It combines Bayesian Optimisation with the Causal Inference Framework to identify the right molecule or marketing tagline.

Here, we present initial work on a crucial extension of CEO, namely the introduction of preference elicitation, an increasingly popular technique in Bayesian Optimisation to elicit crucial causal knowledge from subject matter experts. We introduce the problem of Causal Elicitation for Bayesian Optimisation, discuss elicitation strategies and initial work on empirical evaluation.

Submission Number: 1