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.
Add an accessor and comments explaining the expected lifecycle and use of CausalEstimator objects stored in the CausalModel._estimator_cache dict. Accessor function gains test coverage and slightly simplifies logic via use in CausalModel.estimate_effect().
Add an accessor and comments explaining the expected lifecycle and use of CausalEstimator objects stored in the CausalModel._estimator_cache dict. Accessor function gains test coverage and slightly simplifies logic via use in CausalModel.estimate_effect().
RE Issue #1071 https://github.com/py-why/dowhy/issues/1071