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 is very closely related to the RescaledMedianCDFQuantileScorer but a statistically more well-defined approach. However, it is also slightly more conservative for small sample sizes. Furthermore, it changes the way equal samples are counted in MedianCDFQuantileScorer by also including the test sample itself. This prevents a p-value of 0.
This is very closely related to the RescaledMedianCDFQuantileScorer but a statistically more well-defined approach. However, it is also slightly more conservative for small sample sizes. Furthermore, it changes the way equal samples are counted in MedianCDFQuantileScorer by also including the test sample itself. This prevents a p-value of 0.