Causality in objective world: Directed Acyclic Graphs-based structural parsing / 中华流行病学杂志

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.

Exposure-preceding-outcome regarding time sequence among cohort studies in real world / 中华流行病学杂志

One of the commonly accepted merits of cohort studies (CSs) refers to the exposure precedes outcome superior to other observational designs. We use Directed Acyclic Graphs to construct a causal graph among research populations under CSs. We notice that the substitution of research population in place of a susceptible one can be used for effect estimation. Its correctness depends on the outcome-free status of the substituted population and the performance of both screening and diagnosis regarding the outcomes under study at baseline. The temporal precedence of exposure over outcome occurs theoretically, despite the opposite happens in realities. Correct effect estimate is affected by both the suitability of population substitution and the validities of outcome identification and exclusion.

A structural classification of strategies for confounding control in research design / 中华流行病学杂志

Confounding affects the causal relation among the population. Depending on whether the confounders are known, measurable or measured, they can be divided into four categories. Based on Directed Acyclic Graphs, the strategies for confounding control can be classified as (1) the broken-confounding-path method, which can be further divided into single and dual broken paths, corresponding to exposure complete intervention, restriction and stratification, (2) and the reserved-confounding-path method, which can be further divided into incomplete exposure intervention (in instrumental variable design and non-perfect random control test), mediator method and matching method. Among them, random control test, instrumental variable design or Mendelian randomized design, mediator method can meet the requirements for controlling all four types of confounders, while the restriction, stratification and matching methods are only applicable to known, measurable and measured confounders. Identifying the mechanisms of confounding control is a prerequisite for obtaining correct causal effect estimates, which will be helpful in research design.