An overview of relations among causal modelling methods
An overview of relations among causal modelling methods
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DOI：
10.1093/ije/31.5.1030
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发表时间：
2002-10-01
影响因子：
7.7
通讯作者：
Brumback, B
中科院分区：
文献类型：
--
作者：
Greenland, S;Brumback, B
本文简要概述了健康科学研究中的四种主要因果模型：图形模型（因果图），潜在结果（反事实）模型，因果成分因果模型和结构方程模型。本文着重于不同类型的模型之间的逻辑联系，以及每种方法的不同优势。图形模型可以说明定性的人口假设和来源的偏见不容易看到与其他方法;有害成分的原因模型可以说明具体的假设的作用机制;和潜在的结果和结构方程模型提供了一个基础，定量分析的影响。不同的方法提供了互补的观点，可以一起使用，以改善传统的统计结果的因果解释。
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.