Statistics and Causal Inference
作者:
PaulW. Holland,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 396
页码: 945-960
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478354
出版商: Taylor & Francis Group
关键词: Causal model;Philosophy;Association;Experiments;Mill's methods;Causal effect;Koch's postulates;Hill's nine factors;Granger causality;Path diagrams;Probabilistic causality
数据来源: Taylor
摘要:
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.
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