Evaluation of Causal Effects and Local Structure Learning of Causal Networks

Zhi Geng; Yue Liu; Chunchen Liu; Wang Miao

首页> 外文期刊>Annual Review of Statistics and Its Application >Evaluation of Causal Effects and Local Structure Learning of Causal Networks

【24h】

Evaluation of Causal Effects and Local Structure Learning of Causal Networks

机译：评估因果关系的因果效应与局部结构学习

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Causal effect evaluation and causal network learning are two main research areas in causal inference. For causal effect evaluation; we review the two problems of confounders and surrogates. The Yule-Simpson paradox is the idea that the association between two variables may be changed dramatically due to ignoring confounders. We review criteria for confounders and methods ofadjustment for observed and unobserved confounders. The surrogate paradox occurs when a treatment has a positive causal effect ona surrogate endpoint, which, in turn, has a positive causal effect on a true endpoint, but thetreatmentmayhaveanegativecausaleffectonthetrueendpoint. Someof the existing criteria for surrogates are subject to the surrogate paradox, and we review criteria for consistent surrogates to avoid the surrogate paradox. Causal networks are used to depict the causal relationships among multiple variables. Rather than discovering a global causal network, researchers are ofteninterested indiscoveringthe causes and effects ofa givenvariable. We review some algorithms for local structure learning of causal networks centering around a given variable.

机译：因果效应评估和因果网络学习是因果推断的两个主要研究领域。对于因果效应评估;我们审查了混乱和代理人的两个问题。 Yule-Simpson Paradox是由于忽略混杂器的两个变量之间的关联可能会发生显着变化。我们审查监狱的混淆和方法的标准和不观察到的混乱。替代悖论发生当治疗具有阳性因果效果的替代终点时，这反过来又对真正的终点产生了正面的因果效果，但是母留下的终点效果。一些替代品标准的一些标准受代理悖论的约束，我们审查一致代理人的标准，以避免代理悖论。因果网络用于描述多个变量之间的因果关系。研究人员而不是发现全球因果网络，毫无孤独地陷入困难的原因和影响。我们审查了一些围绕给定变量居中因果网络的局部结构学习的一些算法。

著录项

来源
《Annual Review of Statistics and Its Application》 |2019年第2019期|共22页
作者
Zhi Geng; Yue Liu; Chunchen Liu; Wang Miao;
展开▼
作者单位

School of Mathematical Sciences Peking University Beijing 100871 China;

School of Mathematical Sciences Peking University Beijing 100871 China;

Department of Data Mining NEC Laboratories China Beijing 100600 China;

Guanghua School of Management Peking University Beijing 100871 China;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
causal inference; causal network; confounder; instrumental variable; negative control; local structure learning; surrogate paradox; Yule-Simpson paradox;

机译：因果推断;因果网络;混淆;乐器变量;负控制;当地结构学习;代理悖论;yule-simpson悖论;

相似文献

外文文献
中文文献
专利

1. Evaluation of Causal Effects and Local Structure Learning of Causal Networks [J] . Zhi Geng, Yue Liu, Chunchen Liu, Annual Review of Statistics and Its Application . 2019,第期

机译：评估因果关系的因果效应与局部结构学习
2. Local Causal Network Learning for Finding Pairs of Total and Direct Effects [J] . Yue Liu, Zhuangyan Fang, Yangbo He, Journal of machine learning research . 2020,第a期

机译：用于查找总和直接效果的当地因果关系
3. The Five-Gene-Network Data Analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks [J] . Changwon Yoo, Erik M. Brilz Annals of the New York Academy of Sciences . 2009,第期

机译：基于因果贝叶斯网络的局部因果发现算法的五基因网络数据分析
4. Causal Effects of Landing Parameters on Runway Occupancy Time using Causal Machine Learning Models [C] . Zhi Jun Lim, Sim Kuan Goh, Imen Dhief, IEEE Symposium Series on Computational Intelligence . 2020

机译：使用因果机学习模型对跑道占用时间降落参数的因果影响
5. Causal not confounded gene networks: Inferring acyclic and non-acyclic gene Bayesian networks in mRNA expression studies using recursive V-structures, genetic variation, and orthogonal causal anchor structural equation models. [D] . Aten, Jason Erik. 2008

机译：因果不混淆的基因网络：使用递归V型结构，遗传变异和正交因果锚结构方程模型，在mRNA表达研究中推断无环和非无环基因贝叶斯网络。
6. An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation [O] . Sisi Ma, Patrick Kemmeren, Constantin F. Aliferis, -1

机译：逆向工程基因调控的局部因果途径的主动学习因果发现方法的评价
7. Prior-Knowledge-Driven Local Causal Structure Learning and Its Application on Causal Discovery Between Type 2 Diabetes and Bone Mineral Density [O] . Wei Wang, Gangqiang Hu, Bo Yuan, 2020

机译：先前知识驱动的局部因果结构学习及其在2型糖尿病和骨矿物密度之间的因果发现中

Evaluation of Causal Effects and Local Structure Learning of Causal Networks

摘要

著录项

相似文献

相关主题

期刊订阅