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.
The new parameter allows to indicate whether existing causal mechanisms should be overridden by inferred ones based on the data. This would also include uncertainties of the model selection when computing confidence intervals.
The new parameter allows to indicate whether existing causal mechanisms should be overridden by inferred ones based on the data. This would also include uncertainties of the model selection when computing confidence intervals.