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 PR is the first step toward a general causal prediction API. The API supports Causal, Independent, Confounded, and Selected shifts (individual and multi-attribute settings) currently. The regularization has been implemented using unconditional_reg and conditional_reg functions, which can be used for the general CACM API.
Follow up: implement Phase I of CACM for deriving conditional independence constraints given arbitrary graphs.
This PR is the first step toward a general causal prediction API. The API supports Causal, Independent, Confounded, and Selected shifts (individual and multi-attribute settings) currently. The regularization has been implemented using
unconditional_regandconditional_regfunctions, which can be used for the general CACM API.Follow up: implement Phase I of CACM for deriving conditional independence constraints given arbitrary graphs.