Machine learning (ML) models have been widely used to make predictions.

Discovering causal relations from observational time series without making the stationary assumption is a significant challenge.

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].

On the one hand, "relation discovery" is a

Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables).

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