Statistics and Causal Inference
本文探讨统计学如何用于因果推断，通过一个特定因果模型（Holland和Rubin 1983；Rubin 1974）来批评哲学家、医学研究者、统计学家、计量经济学家和因果建模支持者的相关讨论。
Abstract Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling. Key Words: Causal modelPhilosophyAssociationExperimentsMill's methodsCausal effectKoch's postulatesHill's nine factorsGranger causalityPath diagramsProbabilistic causality This article is part of the following collections: Teaching Simpson’s Paradox, Confounding, and Causal Inference