Sufficient Covariates and Linear Propensity Analysis
Abstract
Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.
Cite
Text
Guo and Dawid. "Sufficient Covariates and Linear Propensity Analysis." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Guo and Dawid. "Sufficient Covariates and Linear Propensity Analysis." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/guo2010aistats-sufficient/)BibTeX
@inproceedings{guo2010aistats-sufficient,
title = {{Sufficient Covariates and Linear Propensity Analysis}},
author = {Guo, Hui and Dawid, Philip},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {281-288},
volume = {9},
url = {https://mlanthology.org/aistats/2010/guo2010aistats-sufficient/}
}