© International Epidemiological Association 2002 Printed in Great Britain THEORY AND METHODS

• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: causal diagrams; causality; confounding; data analy; Greenland
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.122.2449; http://ije.oxfordjournals.org/cgi/reprint/31/5/1030.pdf

overview of relations among causal modelling methods Sander Greenland a and Babette Brumback b Following a long history of informal use in path analysis, causal diagrams (graphical causal models) saw an explosion of theoretical development during the 1990s, 1–3 including elaboration of connections to other methods for causal modelling. The latter connections are especially valuable for those familiar with some but not all methods, as certain background assumptions and sources of bias are more easily seen with certain models, whereas practical statistical procedures may be more easily derived under other models. We provide here a brief overview of graphical causal models, 1–6 the sufficient-component cause (SCC) models of Rothman, 7,8 Ch. 2 the potential-outcome (counterfactual) models now popular in statistics, health, and