# Doubly Robust Inference with Non-probability Survey Samples

arXiv: https://arxiv.org/abs/1805.06432 · HTML: https://mlsanity.com/paper/1805.06432/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 4 of 4)

- [A Directional Rockafellar-Uryasev Regression](https://mlsanity.com/paper/2411.02557/) (2024)
- [Deep Neural Networks for Doubly Robust Estimation with Nonprobability Survey Samples](https://mlsanity.com/paper/2605.28762/) (2026)
- [On efficiency gains via augmenting a tiny sample with a massive auxiliary sample](https://mlsanity.com/paper/2608.26610/) (2026)
- [When Edit Localization Amplifies Relative Selection Bias: Gradient Geometry, Target Mismatch, and Importance Weighting](https://mlsanity.com/paper/2609.13709/) (2026)

## Abstract (arXiv)

We establish a general framework for statistical inferences with non-probability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the non-probability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and the efficiency of our proposed estimators as compared to existing methods. The proposed method is used to analyze a non-probability survey sample collected by the Pew Research Center with auxiliary information from the Behavioral Risk Factor Surveillance System and the Current Population Survey. Our results illustrate a general approach to inference with non-probability samples and highlight the importance and usefulness of auxiliary information from probability survey samples.