Causal Inference in Python 作者: Matheus Facure 出版社: O'Reilly Media, Inc.副标题: Applying Causal Inference in the Tech Industry出版年: 2023-11-30页数: 406定价: S$106.67装帧: PaperbackISBN: 9781098140250豆瓣评分 评价人数不足 评价:
当当轩朗图书专营店在线销售正版《Causal Inference in Python: Applying Causal Inference in the(印刷版)》。最新《Causal Inference in Python: Applying Causal Inference in the(印刷版)》简介、书评、试读、价格、图片等相关信息,尽在DangDang.com,网购《Causal
因果推断的软件工具可以帮助研究者和数据科学家探索和验证变量之间的因果关系。1. Python :- Python是一种多用途的编程语言,拥有丰富的库和框架,CausalDiscoveryToolbox包,专门用于因果推断,支持从观测数据中恢复直接依赖关系和因果关系。DoWhy提供了统一的接口来实现多种因果推断方法,支持从因果图建模到因果效应估计...
Chapter 1. Introduction to Causal Inference In this first chapter I’ll introduce you to a lot of the fundamental concepts of causal inference as well as its main challenges and … - Selection from Causal Inference in Python [Book]
causalinference: 使用Python做因果推断 python虽然与R一样都可以做数据分析,但是在计量方面较为薄弱,python更像是干脏活,清洗数据用的。现在慢慢的python也有一些在计量的包,比如causalinference,这个包可以做因果推断分析。 安装 数据导入 数据描述 x1,x2,x3 协变量(控制变量)...
Causal Inference: What If Author: Miguel Hernan (Harvard University) Year: 2020 Causal Inference in Python Author: Matheus Facure Year: 2023 Causal Analysis Author: Martin Huber (University of Fribourg) Year: 2023 Causal Inference and Discovery in Python ...
Step by step, we introduce the Python causal ecosystem and harness the power of cutting-edge algorithms. In the last part of the book, we sneak into the secret world of causal discovery. We explore the mechanics of how causes leave traces and compare the main families of causal discovery ...
Causal Inference in Python Causal Inference in Python, orCausalinferencein short, is a software package that implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis. ...
因果推断书籍which causal inference book you should read 会飞的鲲 因果推理 Causal Inference 锦恢发表于数据挖掘那... 因果推断边学习边分享(一) 前段时间被导师安利了Judy Pearl的书《The Book of Why》,开始关注因果推断这个领域。分享下自己对因果推断相关方法的学习,不准确之处还请大佬们批评指正。 目录背...
CausalML是一个Python包,它使用基于最近研究的机器学习算法提供了一套增益建模(uplift modeling)和因果推理(causal inference)方法[1]。它提供了一个标准界面,允许用户根据实验或观察数据估计条件平均干预效果(Conditional Average Treatment Effect,CATE)或个体干预效果(Individual Treatment Effect,ITE)。本质上,它估计了在...