Zheng Y J, Zhao N Q, He Y N
Department of Public Health Microbiology of School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Key Laboratory of Health Technology Assessment, Ministry of Health, Fudan University, Shanghai 200032, China.
Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2018 Jan 10;39(1):90-93. doi: 10.3760/cma.j.issn.0254-6450.2018.01.019.
The overall details of causality frames in the objective world remain obscure, which poses difficulty for causality research. Based on the temporality of cause and effect, the objective world is divided into three time zones and two time points, in which the causal relationships of the variables are parsed by using Directed Acyclic Graphs (DAGs). Causal DAGs of the world (or causal web) is composed of two parts. One is basic or core to the whole DAGs, formed by the combination of any one variable originating from each time unit mentioned above. Cause effect is affected by the confounding only. The other is an internal DAGs within each time unit representing a parent-child or ancestor-descendant relationship, which exhibits a structure similar to the confounding. This paper summarizes the construction of causality frames for objective world research (causal DAGs), and clarify a structural basis for the control of the confounding in effect estimate.
客观世界中因果关系框架的整体细节仍不明确,这给因果关系研究带来了困难。基于因果关系的时间性,将客观世界划分为三个时区和两个时间点,通过有向无环图(DAG)来解析变量之间的因果关系。世界的因果DAG(或因果网络)由两部分组成。一部分是整个DAG的基础或核心,由上述每个时间单位中任意一个变量组合而成。因果关系仅受混杂因素影响。另一部分是每个时间单位内的内部DAG,代表父子或祖先-后代关系,其结构与混杂因素相似。本文总结了用于客观世界研究的因果关系框架(因果DAG)的构建,并阐明了在效应估计中控制混杂因素的结构基础。