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 uses an improved version of the "twice the average" rule following recent results from M. Gasparini, R. Wang, and A. Ramdas, Combining exchangeable p-values, arXiv 2404.03484, 2024.
This new method is now used by default when merging p-values. Accordingly, the quantile based method was renamed to be more consistent with the naming pattern.
This uses an improved version of the "twice the average" rule following recent results from M. Gasparini, R. Wang, and A. Ramdas, Combining exchangeable p-values, arXiv 2404.03484, 2024.
This new method is now used by default when merging p-values. Accordingly, the quantile based method was renamed to be more consistent with the naming pattern.