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.
These functions allow causal inference for temporal data. These helper functions help pre-process the dataframe according to the discovered temporal causal graph, which can be further used for causal effect estimation.
These functions allow causal inference for temporal data. These helper functions help pre-process the dataframe according to the discovered temporal causal graph, which can be further used for causal effect estimation.