Introductive remarks on casual inference

Silvana A. Romio, Rino Bellocco and Giovanni Corrao

Statistica, 2010, vol. 70, issue 3, 354-362

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.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:70:y:2010:i:3:p:354-362

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