#计算机科学#DoWhy是微软开发的一个用于因果推断的Python库，旨在引发因果关系思考和分析
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
Automatically determine whether a causal effect is identifiable
This repository contains an implementation of BP-CDM introduced in "Data-Driven Decision Support for Business Processes: Causal Reasoning on Interventions".
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models
Basic demonstration of causal effects for Pearl's do-calculus