Causality in objective world: Directed Acyclic Graphs-based structural parsing.
10.3760/cma.j.issn.0254-6450.2018.01.019
- Author:
Y J ZHENG
1
;
N Q ZHAO
2
;
Y N HE
1
Author Information
1. 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.
2. Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.
- Publication Type:Journal Article
- Keywords:
Causal Web;
Causality;
Confounding;
Directed Acyclic Graphs;
Temporality
- MeSH:
Causality;
Computer Graphics;
Confounding Factors, Epidemiologic;
Data Interpretation, Statistical;
Epidemiologic Methods;
Humans
- From:Chinese Journal of Epidemiology
2018;39(1):90-93
- CountryChina
- Language:Chinese
-
Abstract:
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.
