We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling target and constraint quantities using Gaussian processes and by sequentially selecting interventions via a constrained expected improvement acquisition function. We propose different surrogate models that enable to integrate observational and interventional data while capturing correlation among effects with increasing levels of sophistication. We evaluate cCBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions.

翻译：我们提出约束因果贝叶斯优化（cCBO），该方法旨在已知因果图中寻找满足特定约束条件下优化目标变量的干预措施。cCBO首先通过利用图结构及（若存在）观测数据集来缩小搜索空间；随后通过高斯过程建模目标与约束量，并借助约束期望改进采集函数顺序选择干预措施，从而求解简化后的优化问题。我们提出了多种替代代理模型，这些模型能够整合观测数据与干预数据，同时以渐增的复杂度捕捉效应间的相关性。我们在人工构建与真实世界的因果图上评估cCBO，展示了其在快速收敛与可行干预比例之间的成功权衡。