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Introductive remarks on casual inference

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  • Silvana A. Romio
  • Rino Bellocco
  • Giovanni Corrao

Abstract

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.

Suggested Citation

  • Silvana A. Romio & Rino Bellocco & Giovanni Corrao, 2010. "Introductive remarks on casual inference," Statistica, Department of Statistics, University of Bologna, vol. 70(3), pages 354-362.
    • Handle: RePEc:bot:rivsta:v:70:y:2010:i:3:p:354-362
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