Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model

David Madigan

Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model

David Madigan

Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.

Abstract

In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a Bayesian graphical model facilitates model communication and computation.

Cite this Paper

BibTeX


@InProceedings{pmlr-vR2-madigan99a,
  title = 	 {Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model},
  author =       {Madigan, David},
  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/madigan99a/madigan99a.pdf},
  url = 	 {https://proceedings.mlr.press/r2/madigan99a.html},
  abstract = 	 {In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a Bayesian graphical model facilitates model communication and computation.},
  note =         {Reissued by PMLR on 20 August 2020.}
}

Endnote

%0 Conference Paper
%T Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model
%A David Madigan
%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-madigan99a
%I PMLR
%U https://proceedings.mlr.press/r2/madigan99a.html
%V R2
%X In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a Bayesian graphical model facilitates model communication and computation.
%Z Reissued by PMLR on 20 August 2020.

APA


Madigan, D.. (1999). Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model. 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/madigan99a.html. Reissued by PMLR on 20 August 2020.

Related Material

Download PDF