An experiment in causal discovery using a pneumonia database

Peter Spirtes, Gregory F. Cooper
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.

Abstract

We tested a causal discovery algorithm on a database of pneumonia patients. The output of the causal discovery algorithm is a list of statements "A causes B", where A and B are variables in the database, and a score indicating the degree of confidence in the statement. We compared the output of the algorithm with the opinions of physicians about whether A caused B or not. We found that the doctors opinions were independent of the output of the algorithm. However, an examination of the output of results suggested a simple, well motivated modification of the algorithm which would bring the output of the algorithm into high agreement with the physicians opinions.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR2-spirtes99a, title = {An experiment in causal discovery using a pneumonia database}, author = {Spirtes, Peter and Cooper, Gregory F.}, booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics}, year = {1999}, editor = {Heckerman, David and Whittaker, Joe}, volume = {R2}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r2/spirtes99a/spirtes99a.pdf}, url = {https://proceedings.mlr.press/r2/spirtes99a.html}, abstract = {We tested a causal discovery algorithm on a database of pneumonia patients. The output of the causal discovery algorithm is a list of statements "A causes B", where A and B are variables in the database, and a score indicating the degree of confidence in the statement. We compared the output of the algorithm with the opinions of physicians about whether A caused B or not. We found that the doctors opinions were independent of the output of the algorithm. However, an examination of the output of results suggested a simple, well motivated modification of the algorithm which would bring the output of the algorithm into high agreement with the physicians opinions.}, note = {Reissued by PMLR on 20 August 2020.} }
Endnote
%0 Conference Paper %T An experiment in causal discovery using a pneumonia database %A Peter Spirtes %A Gregory F. Cooper %B Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1999 %E David Heckerman %E Joe Whittaker %F pmlr-vR2-spirtes99a %I PMLR %U https://proceedings.mlr.press/r2/spirtes99a.html %V R2 %X We tested a causal discovery algorithm on a database of pneumonia patients. The output of the causal discovery algorithm is a list of statements "A causes B", where A and B are variables in the database, and a score indicating the degree of confidence in the statement. We compared the output of the algorithm with the opinions of physicians about whether A caused B or not. We found that the doctors opinions were independent of the output of the algorithm. However, an examination of the output of results suggested a simple, well motivated modification of the algorithm which would bring the output of the algorithm into high agreement with the physicians opinions. %Z Reissued by PMLR on 20 August 2020.
APA
Spirtes, P. & Cooper, G.F.. (1999). An experiment in causal discovery using a pneumonia database. Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R2 Available from https://proceedings.mlr.press/r2/spirtes99a.html. Reissued by PMLR on 20 August 2020.

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