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.

Causing: CAUsal INterpretation using Graphs

Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data

"Causality: Models, Reasoning, and Inference-Judea Pearl(2009)"中文翻译及学习笔记

A Python implementation of the do-calculus of Judea Pearl et al.

A Powerful Python Library for Causal Inference

This repository contains an implementation of BP-CDM introduced in "Data-Driven Decision Support for Business Processes: Causal Reasoning on Interventions".

Memo's research works.

Bayesian Causal Inference in Doubly Gaussian DAG-probit Models

Summary of useful results in Causal Inference

Automatically determine whether a causal effect is identifiable

Basic demonstration of causal effects for Pearl's do-calculus