期刊
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
卷 31, 期 5, 页码 1030-1037出版社
OXFORD UNIV PRESS
DOI: 10.1093/ije/31.5.1030
关键词
Bias; causal diagrams; causality; confounding; data analysis; direct effects; epidemiological methods; graphical models; inference; instrumental variables; risk analysis; sufficient-component cause models; structural equations
This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据