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Statistics and Causal Inference

Paul W. Holland
Journal of the American Statistical Association
Vol. 81, No. 396 (Dec., 1986), pp. 945-960
DOI: 10.2307/2289064
Stable URL: http://www.jstor.org/stable/2289064
Page Count: 16
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Statistics and Causal Inference
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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.

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