Multiply robust dose-response estimation for multivalued causal inference problems
Methodology
2017-05-18 v2
Abstract
This paper develops a multiply robust (MR) dose-response estimator for causal inference problems involving multivalued treatments. We combine a family of generalised propensity score (GPS) models and a family of outcome regression (OR) models to achieve an average potential outcomes estimator that is consistent if just one of the GPS or OR models in each family is correctly specified. We provide proofs and simulations that demonstrate multiple robustness in the context of multivalued causal inference problems.
Cite
@article{arxiv.1611.02433,
title = {Multiply robust dose-response estimation for multivalued causal inference problems},
author = {Cian Naik and Emma J. McCoy and Daniel J. Graham},
journal= {arXiv preprint arXiv:1611.02433},
year = {2017}
}