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Statistics and Causal Inference
Journal of the American Statistical Association, 81 (396): 945--960 (1986)ArticleType: research-article / Full publication date: Dec., 1986 / Copyright © 1986 American Statistical Association.
DOI: 10.2307/2289064
Аннотация
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.
