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.

翻译：因果发现对于理解复杂系统和指导决策至关重要。虽然观测数据能在特定假设下揭示因果关系，但其往往存在局限，使得主动干预成为必要。现有方法（如贝叶斯和图论方法）未将决策优化作为核心目标，且常依赖于理想条件或与假设检验无直接关联的信息增益指标。受贝叶斯因子启发，我们提出一种新颖的基于贝叶斯优化的方法，旨在最大化获得决定性正确证据的概率。该方法利用观测数据估计不同假设下的因果模型，通过预实验评估潜在干预措施，并迭代更新先验分布以优化干预策略。我们通过多组实验验证了该方法的有效性。本研究的贡献在于构建了一个通过主动干预实现高效因果发现的鲁棒框架，推动了理论进展在实际应用中的落地。