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

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.

PDF AISTATS Semantic Scholar

Cite

Text

Madigan. "Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.

Markdown

[Madigan. "Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.](https://mlanthology.org/aistats/1999/madigan1999aistats-bayesian/)

BibTeX

@inproceedings{madigan1999aistats-bayesian,
  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},
  volume    = {R2},
  url       = {https://mlanthology.org/aistats/1999/madigan1999aistats-bayesian/}
}