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International Journal of Epidemiology 2002;31:1030-1037
© International Epidemiological Association 2002

Theory and Methods

An overview of relations among causal modelling methods

Sander Greenlanda and Babette Brumbackb

a Department of Epidemiology, UCLA School of Public Health, Department of Statistics, UCLA College of Letters and Science, 22333 Swenson Drive, Topanga, CA 90290–3434, USA. E-mail: lesdomes{at}ucla.edu
b Department of Biostatistics, University of Washington, School of Public Health and Community Medicine, Seattle, WA 98195, USA.

This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.

Keywords Bias, causal diagrams, causality, confounding, data analysis, direct effects, epidemiological methods, graphical models, inference, instrumental variables, risk analysis, sufficient-component cause models, structural equations

Accepted 28 March 2002


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