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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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Paul W. Holland (Mon,) studied this question.
www.synapsesocial.com/papers/69d9612304deaa6ab5684336 — DOI: https://doi.org/10.1080/01621459.1986.10478354
Paul W. Holland
Journal of the American Statistical Association
Educational Testing Service
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