  Causal discovery is crucial for understanding complex systems and informing
decisions. While observational data can uncover causal relationships under
certain assumptions, it often falls short, making active interventions
necessary. Current methods, such as Bayesian and graph-theoretical approaches,
do not prioritize decision-making and often rely on ideal conditions or
information gain, which is not directly related to hypothesis testing. We
propose a novel Bayesian optimization-based method inspired by Bayes factors
that aims to maximize the probability of obtaining decisive and correct
evidence. Our approach uses observational data to estimate causal models under
different hypotheses, evaluates potential interventions pre-experimentally, and
iteratively updates priors to refine interventions. We demonstrate the
effectiveness of our method through various experiments. Our contributions
provide a robust framework for efficient causal discovery through active
interventions, enhancing the practical application of theoretical advancements.
