# Multiply robust dose-response estimation for multivalued causal inference problems

arXiv: https://arxiv.org/abs/1611.02433 · HTML: https://mlsanity.com/paper/1611.02433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference](https://mlsanity.com/paper/2307.10536/) (2023)

## Abstract (arXiv)

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.