[May cross-sectional studies provide causal inferences?]

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.基于变量调查（或测量）的共时性、统计学关联及幸存者偏倚等原因，横断面研究被认为不能进行因果推断，这是当前的"共识"。本文基于因果思维，借助因果图，首先明确定义真实截面和测量截面，并识别截面概念仅存在于理论的特性。实际横断面研究中，测量变量的共时性并不存在，而是无一例外地表现为非共时性时序，其实质上相当于测量变量间互为独立性假设，或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究，横断面研究均为暴露和结局已存在或发生后进行的测量，这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是，测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者，测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。.