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

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

Summary of useful results in Causal Inference

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 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.

Automatically determine whether a causal effect is identifiable

Bayesian Causal Inference in Doubly Gaussian DAG-probit Models

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