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.
The build sometimes randomly fails due to a timeout issue in the unit tests of the unit change methods of the GCM module. While this only happens in the github builds, this is most likely due to the prallelization of the underlying RandomForestRegressors being fitted.
The build sometimes randomly fails due to a timeout issue in the unit tests of the unit change methods of the GCM module. While this only happens in the github builds, this is most likely due to the prallelization of the underlying RandomForestRegressors being fitted.