CiteWeb id: 20120000067

CiteWeb score: 3319

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.

The publication "Statistics and Causal Inference" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 1000. Additionally, the publicaiton "Statistics and Causal Inference" is placed in the Top 100 among other scientific works published in 2012.
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