Transfer Learning and Anomaly Attribution

Hey,

Causica looks super promising, thanks for making open!
I've been playing around for a few days now trying to get a good grasp of it, and I there a couple points that are a bit unclear for me:

1. How could I do transfer learning/fine-tune. I've trained a model with the entirety of the data, but I'd like to fine tune it for a single customer that potentially doesnt have enough data to train it only on his data.
2. How could I do anomaly attribution or distributional changes attribution. ie something changed could the model tell me what was the root cause of it? So far the only way I could find was to take the causica-generated graph to dowhy to perform this, but this means, I think, that I'd loose the forward methods as dowhy does not work with them.

Looking forward to hearing any suggestions!

Cheers,
Jason