A new classification of measured temporalities: based on the time axis in nature
10.3760/cma.j.cn112338-20190929-00711
- VernacularTitle:基于大自然时间轴的测量时序分类法
- Author:
Tianlei WANG
1
;
Yutong MOU
;
Hui KAN
;
Yaxin LI
;
Wei FAN
;
Jianghong DAI
;
Yingjie ZHENG
Author Information
1. 复旦大学公共卫生学院流行病学教研室，国家卫生健康委员会卫生技术评估重点实验室，公共卫生安全教育部重点实验室，上海 200032
- Keywords:
Causal thinking;
Causal temporality;
Measured temporality;
Causal inference;
Epidemiology;
Observation;
Experiment
- From:
Chinese Journal of Epidemiology
2020;41(5):782-787
- CountryChina
- Language:Chinese
-
Abstract:
In causal inference, the concept of temporality (or directionality) has not been fully clarified. Starting from causal thinking, this paper divides the time axis in nature into three time domains and two time points by the occurrence timings of both a real cause and a real effect. This has anchored that causal inference can only be realized in the third domain. The measured temporalities can be divided into five types: cross-first-to-third-domain longitudinal (or experimental temporalities), cross-second-to-third-domain longitudinal, within-domain longitudinal, within-domain reversely longitudinal, and within-domain transversal (or observational temporalities). This new classification encompasses all measurement strategies, either for first or multiple measurements, or timely and delayed measurements. Except that the actual measurement for the cause occurs either before its occurrence (only in experiment) or within the second domain, all other measurements are similar to the act of historical reconstruction or "archaeology" , where the importance of measured temporalities may be inferior to the accuracy of the measurements. From the point of view that research design should integrate bias design, this new classification for measured temporalities based on the time axis in Nature, which has a clear meaning and helps to judge the possible biases in the observation methods, provides a basis for correct causal inferences.
