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.
Before, when a causal graph had causal mechanisms assigned, they were also used when creating a new GCM object based on it. Now, they are removed (from a copied version of the graph) if the new flag is set to True.
Before, when a causal graph had causal mechanisms assigned, they were also used when creating a new GCM object based on it. Now, they are removed (from a copied version of the graph) if the new flag is set to True.