Consistent Estimation Given Missing Data
Abstract
This paper presents a unified approach for recovering causal and probabilistic queries using graphical models given missing (or incomplete) data. To this end, we develop a general algorithm that can recover conditional probability distributions and conditional causal effects in semi-Markovian models.
Cite
Text
Mohan and Pearl. "Consistent Estimation Given Missing Data." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.Markdown
[Mohan and Pearl. "Consistent Estimation Given Missing Data." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.](https://mlanthology.org/pgm/2018/mohan2018pgm-consistent/)BibTeX
@inproceedings{mohan2018pgm-consistent,
title = {{Consistent Estimation Given Missing Data}},
author = {Mohan, Karthika and Pearl, Judea},
booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models},
year = {2018},
pages = {284-295},
volume = {72},
url = {https://mlanthology.org/pgm/2018/mohan2018pgm-consistent/}
}