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.
Changing from
"Getting Starte" - "User Guide" - "Examples" - "dowhy package" - "Contributing" - "Release notes" - "Citing this package" to
"Getting Starte" - "User Guide" - "Examples" - "Citing this package" - "Contributing" - "Dowhy package" "Release notes"
Here, the last two entries are now 'hidden' under a "more" tab, i.e., putting the more important information to the front. Source references might be easier to find via the search function.
Changing from "Getting Starte" - "User Guide" - "Examples" - "dowhy package" - "Contributing" - "Release notes" - "Citing this package" to "Getting Starte" - "User Guide" - "Examples" - "Citing this package" - "Contributing" - "Dowhy package" "Release notes"
Here, the last two entries are now 'hidden' under a "more" tab, i.e., putting the more important information to the front. Source references might be easier to find via the search function.