Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Mar 19;9(1):1.
doi: 10.1186/1742-7622-9-1.

Causal diagrams in systems epidemiology

Michael Joffe  1 , Manoj Gambhir, Marc Chadeau-Hyam, Paolo Vineis
Affiliations

Causal diagrams in systems epidemiology

Michael Joffe et al. Emerg Themes Epidemiol. .

Abstract

Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Pearl: causal & statistical languages.
Figure 2
Figure 2
DAGs representing the relationship of the variables X, Y and Z.
Figure 3
Figure 3
A flow diagram of the SIR model.
Figure 4
Figure 4
A flow diagram illustrating a rate-limiting step.
Figure 5
Figure 5
The full-chain approach in environmental and occupational epidemiology.
Figure 6
Figure 6
An example of the web of causation.
Figure 7
Figure 7
A causal diagram used as the basis for statistical analysis.
Figure 8
Figure 8
Socioeconomic status and biological fertility.
Figure 9
Figure 9
Conditional independence.
Figure 10
Figure 10
Instrumental variables.
Figure 11
Figure 11
C-reactive protein as a biomarker.
Figure 12
Figure 12
A change model of the web of causation.
Figure 13
Figure 13
A dangerous bend: risk compensation.
Figure 14
Figure 14
The situation of a household under conditions of absolute poverty.
See this image and copyright information in PMC

References

    1. Breslow NE. Are statistical contributions to medicine undervalued? Biometrics. 2003;59:1–8. doi: 10.1111/1541-0420.00001. - DOI - PubMed
    1. Epstein P. Changing planet, changing health. University of California Press; 2011.
    1. Pearl J. Causality: models, reasoning and inference. New York: Cambridge University Press; 2000.
    1. Pearl J. Causal inference in the health sciences: a conceptual introduction. Health services and outcomes research methodology. 2002;2:189–220.
    1. Joffe M. In: Explanation, Prediction, and Confirmation. New Trends and Old Ones Reconsidered. Dieks D, Wenceslao JG, Hartmann S, Uebel T, Weber M, editor. Springer; 2011. Causality and evidence discovery in epidemiology.

LinkOut - more resources