10.6092/ISSN.1973-2201/3591
ROMIO, SILVANA A.; Department Of Medical Informatics Erasmus Me Department Of Statistics-University Of Milano-Bicocca
BELLOCCO, RINO; Department Of Medical Epidemiology And Biostatistics -Karolinska Institutet
CORRAO, GIOVANNI; Department Of Statistics-University Of Milano-Bicocca
Introductive remarks on causal inference
Dep. of Statistical Sciences "Paolo Fortunati", Università di Bologna
2013
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2013
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Statistica; Vol 70, No 3 (2010); 353-362
One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.
Statistica; Vol 70, No 3 (2010); 353-362