  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.
