We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare. We propose Causal Entropy Optimization (CEO), a framework that generalizes Causal Bayesian Optimization (CBO) to account for all sources of uncertainty, including the one arising from the causal graph structure. CEO incorporates the causal structure uncertainty both in the surrogate models for the causal effects and in the mechanism used to select interventions via an information-theoretic acquisition function. The resulting algorithm automatically trades-off structure learning and causal effect optimization, while naturally accounting for observation noise. For various synthetic and real-world structural causal models, CEO achieves faster convergence to the global optimum compared with CBO while also learning the graph. Furthermore, our joint approach to structure learning and causal optimization improves upon sequential, structure-learning-first approaches.
翻译:我们研究全球优化对可以进行干预的未知因果图目标变量的因果影响的问题,这个问题出现在许多科学领域,包括生物学、业务研究和保健领域。我们提议Causal Entropy Optimination(CEO),这是一个普遍化Causal Bayesian Optimization(CBO)的框架,以说明所有不确定性的来源,包括因果图结构产生的不确定性。CEO将因果结构的替代模型和通过信息理论获取功能选择干预措施的机制中的因果结构不确定性纳入其中。由此产生的算法自动交换结构学习和因果效果优化,同时自然核算观测噪音。对于各种合成和现实世界结构因果模型,CEO在学习该图的同时,更快地实现与全球最佳的趋同。此外,我们在结构学习和因果优化方面的共同方法改善了按顺序、结构-学习-第一种方法。