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.
In v0.10, the user is expected to provide only the effect modifiers columns, which can cause issues if the order of effect modifiers is not correct.
So we revert to v0.8 behavior where the user is only expected to pass the dataframe.
Fixes #1038.
In v0.10, the user is expected to provide only the effect modifiers columns, which can cause issues if the order of effect modifiers is not correct. So we revert to v0.8 behavior where the user is only expected to pass the dataframe.