Li Yijie, Kan Hui, He Yining, Li Yaxin, Mu Yutong, Dai Jianghong, Zheng Yingjie
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.
Key words:
Causal thinking; Cross-section; Measured temporal orders; Causal inference; Epidemiology; Observation
Contributor Information
Li Yijie
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
Kan Hui
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
He Yining
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
Li Yaxin
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
Mu Yutong
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
Dai Jianghong
Department of Epidemiology and Biostatistics, School of Public Health, Xinjiang Medical University, Urumqi 830011, China
Zheng Yingjie
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning, Fudan University, Shanghai 200032, China
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
