A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data originating from different experimental environments, improves identifiability. However, the improvement depends critically on the target and nature of the interventions carried out in each experiment. Since in real applications experiments tend to be costly, there is a need to perform the right interventions such that as few as possible are required. In this work we propose a new active learning (i.e. experiment selection) framework (A-ICP) based on Invariant Causal Prediction (ICP) (Peters et al., 2016). For general structural causal models, we characterize the effect of interventions on so-called stable sets, a notion introduced by (Pfister et al., 2019). We leverage these results to propose several intervention selection policies for A-ICP which quickly reveal the direct causes of a response variable in the causal graph while maintaining the error control inherent in ICP. Empirically, we analyze the performance of the proposed policies in both population and finite-regime experiments.

翻译：在实际应用实验中,由于实际应用实验往往费用高昂,因此需要尽可能少地进行正确的干预。在这项工作中,我们根据Invariant Causal预测(IPC)提出一个新的积极学习(即实验选择)框架(A-ICP)(IPC)(Peters等人,2016年)。关于一般结构性因果关系模型,我们分析干预对所谓的稳定组合的影响,这是(Pfister等人,2019年)提出的概念。我们利用这些结果为A-ICP提出若干干预选择政策,为A-ICP提出若干干预选择政策,该政策迅速揭示因果图中反应变数的直接原因,同时保持比较方案固有的错误控制。