Sufficient covariates and linear propensity analysis


Hui Guo, Philip Dawid ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:281-288, 2010.


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.

Related Material