May cross-sectional studies provide causal inferences?
10.3760/cma.j.cn112338-20191030-00770
- VernacularTitle:横断面研究能否进行因果推断
- Author:
Yijie LI
1
;
Hui KAN
;
Yining HE
;
Yaxin LI
;
Yutong MU
;
Jianghong DAI
;
Yingjie ZHENG
Author Information
1. 复旦大学公共卫生学院流行病学教研室，上海 200032；国家卫生健康委员会卫生技术评估重点实验室（复旦大学），上海 200032；复旦大学公共卫生学院公共卫生安全教育部重点实验室，上海 200032
- Keywords:
Causal thinking;
Cross-section;
Measured temporal orders;
Causal inference;
Epidemiology;
Observation
- From:
Chinese Journal of Epidemiology
2020;41(4):589-593
- CountryChina
- Language:Chinese
-
Abstract:
Due to the flaws inherited in synchronicity, statistical association and survivor bias on variables under measurement, a common 'consensus’ has been reached on "cross-sectiional studies (CSS) can lead to failure on causal inference". In this paper, under both causal thinking and diagram, the real and measured cross-sections are clearly defined that these two concepts only exist theoretically. In real CSS research, the temporal orders of measured variables are all non-synchronic, equivalent to the assumption that measurement variables are independent to each other, or there is no differentiated classification bias. Similar to cumulative case-control or historical cohort studies, both exposure and outcome that exist or occur before their measurements in cross-sectional studies, are actions of historical reconstruction or doing 'Archaeology’. One of the common preconditions for causal inference in such studies is that: there must be a causal relation between the measured variables and their historical counterparts. The measured variables are all agents of their corresponding real counterparts, and the temporal orders are not that important in causal inference. It is necessary to better understand the analytic role of the CSS.
