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 draft PR addresses this issue, by introducing the doubly robust estimator to doWhy. It also contains a tutorial notebook applying this algorithm to a demo dataset. This is a rough draft, so there will be lots of changes!
This draft PR addresses this issue, by introducing the doubly robust estimator to doWhy. It also contains a tutorial notebook applying this algorithm to a demo dataset. This is a rough draft, so there will be lots of changes!