DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
This should significantly speed up some calculations for larger graphs. In this regard, also increased the default samples for the anomaly attribution to balance the reduced number of Shapley run.
Tested on the notebooks and got equal results as before.
This should significantly speed up some calculations for larger graphs. In this regard, also increased the default samples for the anomaly attribution to balance the reduced number of Shapley run.
Tested on the notebooks and got equal results as before.