For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.

The study of causal relations can be seen through the lens of learning andinference [16,21].

Thistask is challenging because the causal graph is unknown and even there may exist latent confounders.

Asinterventions areexpensive(require carefully controlled experiments) andperforming multiple interventions is time-consuming, an important goal in causal discovery is to design algorithms that utilize simple (preferably, single variable) and fewer interventions [Shanmugam et al.,2015]. However, when there are latents or unobserved variables in the system, in the worst-case, it is not possible to learn the exact causal DAG without intervening on every variable at least once.