Active Invariant Causal Prediction: Experiment Selection through Stability

One 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 talk, I will present new active learning (i.e. experiment selection) strategies based on causal invariance, which select intervention targets that quickly reveal the direct causes of a response variable of interest in the causal graph.

https://arxiv.org/pdf/2006.05690.pdf

https://youtu.be/u8uajHdY6Dw