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 feature will enable user to write custom equations for each and node and get a causal model back with causal mechanisms assigned.
The Usage is mainly targeted for when the function/relationship model between nodes is known and allows user to specify it in a equation form as demonstrated below -
X = empirical()
Y = 12*exp(X) + halfnorm()
Z = 3*Y + empirical()
List of Supported functions for specifying parent-child relationships - here
List of Supported functions for specifying noise models here
List of Supported functions for specifying parent-child relationships - here
List of Supported functions for specifying noise models here