Nihon eiseigaku zasshi. Japanese journal of hygiene
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Nihon Eiseigaku Zasshi · Sep 2009
[Causal inference in medicine part I--counterfactual models--an approach to clarifying discussions in research and applied public health].
A central problem in natural science is identifying general laws of cause and effect. Medical science is devoted to revealing causal relationships in humans. The framework for causal inference applied in epidemiology can contribute substantially to clearly specifying and testing causal hypotheses in many other areas of biomedical research. ⋯ In observational studies, however, there is a greater risk that the assumption of conditional exchangeability may be violated. In summary, in this article, we highlight the following points: (1) individual causal effects cannot be inferred because counterfactual outcomes cannot, by definition, be observed; (2) the distinction between concepts of association and concepts of causation and the basis for the definition of confounding; (3) the importance of elaborating specific research hypotheses in order to evaluate the assumption of conditional exchangeability between the exposed and unexposed groups; (4) the advantages of defining research hypotheses at the population level, including specification of a hypothetical intervention, consistent with the counterfactual model. In addition, we show how understanding the counterfactual model can lay the foundation for correct interpretation of epidemiologic evidence.
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Nihon Eiseigaku Zasshi · Sep 2009
[Causal Inference in Medicine Part II. Directed acyclic graphs--a useful method for confounder selection, categorization of potential biases, and hypothesis specification].
Confounding is frequently a primary concern in epidemiological studies. With the increasing complexity of hypothesized relationships among exposures, outcomes, and covariates, it becomes very difficult to present these hypotheses lucidly and comprehensively. Graphical models are of great benefit in this regard. ⋯ A proper interpretation of the coefficients of a statistical model for addressing a specific research hypothesis relies on an accurate specification of a causal DAG reflecting the underlying causal structure. Unless DAGs correspond to research hypotheses, we cannot reliably reach proper conclusions testing the research hypotheses. Finally, (3) we have briefly reviewed other approaches to causal inference, and illustrate how these models are connected.