Causal diagrams: pitfalls and tips.

Causal diagrams: pitfalls and tips.
J Epidemiol. 2020 Feb 01;:
Authors: Suzuki E, Shinozaki T, Yamamoto E
Abstract
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs; 1) Each node on DAGs corresponds to a random variable, and not its realized values; 2) The presence or absence of arrow in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of ...

Source: Journal of Epidemiology - February 7, 2020 Category: Epidemiology Tags: J Epidemiol Source Type: research