# Statistics and Causal Inference

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.

**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.

- www.tc.umn.edu/~alonso/Holland_1986.pdf
- www.ipc-undp.org/evaluation/aula1-causalidade/Rubin%20-%20Comments%20on%20Causal%20Inference.pdf
- www.ics.uci.edu/~sternh/courses/265/holland_jasa1986.pdf
- www.jstor.org/stable/2289064?origin=crossref
- www.tandfonline.com/doi/abs/10.1080/01621459.1986.10478354
- dx.doi.org/10.1080/01621459.1986.10478354
- amstat.tandfonline.com/doi/abs/10.1080/01621459.1986.10478354
- www.nber.org/~rdehejia/Ec486/slides%20and%20papers/Lecture%206%20Holland%201986.pdf

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