Revealing the causal structure in time-dependent data

We have a data table that accumulates the control and monitoring parameters of the High-Temperature Superconductor (HTS) production process: such that the rows represent the observations and columns represent the parameters mentioned above.

Due to the nature of the production process, there are time dependencies between the rows of our data sets. Thus the columns, are, indeed, time series. (Which boils down our data to time-dependent data.)

Now the question arises: whether we can apply induced causation methods, explained in sections 2.5 and 2.6 of [1], also publicly available and explained here, and implemented in this GitHub repo, to reveal the causal structure of the time-series data set, or are there better options?

[1] Pearl, J., 2009. Causality. Cambridge university press.