Open-source alternatives to Causality

This repository provides a collection of Python implementations for causal inference, designed to estimate the impact of specific interventions using observational data. It serves as a statistical toolkit for researchers to isolate causal signals from complex confounding factors in data sets that lack experimental control. The framework enables the application of rigorous methodologies to study health determinants and evaluate policy interventions. By utilizing structural causal modeling and directed acyclic graphs, the library allows users to map causal dependencies and identify the necessar

DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential four-step framework: modeling, identification, estimation, and refutation. It treats causal assumptions as explicit, first-class citizens, represented as directed acyclic graphs that can be automatically validated against observed data. The library distinguishes itself by cleanly separating the causal identification problem from statistical estimation, allowing any compatible estimator to be used for a given target estimand. It includes automated refutation testing that validates