An introduction to causal inference.
An introduction to causal inference.
复制标题
DOI：
10.2202/1557-4679.1203
复制
发表时间：
2010-02-26
期刊：
影响因子：
--
通讯作者：
Pearl, Judea
中科院分区：
文献类型：
--
作者：
Pearl, Judea
本文总结了因果推理的最新进展，并强调了从传统的统计分析到多元数据因果分析必须进行的范式转变。特别强调的是所有因果推理的基础假设，在制定这些假设，所有因果和反事实索赔的条件性质，以及已开发的评估这些索赔的方法所使用的语言。Pearl（2000 a）基于结构因果模型（SCM）的一般因果理论说明了这些进展，SCM包含并统一了因果关系的其他方法，并为原因和反事实的分析提供了连贯的数学基础。特别是，该论文调查了用于推断（从数据和假设的组合）三种类型因果查询答案的数学工具的发展：关于（1）潜在干预措施的影响，（2）反事实的概率，以及（3）直接和间接影响（也称为“调解”）。最后，本文定义了结构框架和潜在结果框架之间的正式和概念关系，并提出了使用两者强大功能的共生分析工具。这些工具在对调解、影响的原因和因果关系的概率的分析中得到了证明。
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.