Transfer Learning and Anomaly Attribution about causica HOT 1 OPEN

Thanks for raising this @jspiliot!

1. Do you have a similar dataset that you could use to train this with? It's not quite clear what type of fine-tuning you're referring to. In the simplest case where the variables are the same for a bigger dataset and the single customer with a smaller dataset - you could simply train on the larger dataset and assume that the causal connections generalise between the customers (and then potentially fine tune on the specific customer). If the variables are not the same, you might still be lucky that you can make use of the composability of causal mechanisms. Do you have more insights into you problem setup?
2. Similarly, what is your setup here? Generally, once you have trained a DECI model, you could calculate different counterfactuals to the sample that is an outlier and see which (minimal) change makes it behave "more normal". We don't have an explicit root-cause analysis module built but the general functionality should be present.
   Cheers,
   Nick

from causica.