Unbiased Survey Estimation with Population Auxiliary Variables

Oregon Convention Center 

In many applications, population auxiliary variables and predictive models can be used to increase the precision and accuracy of survey estimates. We propose a new model-assisted approach that makes it possible incorporate model predictions into survey estimation to improve precision, while maintaining the unbiasedness property of the Horvitz-Thompson estimator. Our method allows for any prediction function or machine learning algorithm to be used to predict the response for out-of-sample observations. The unbiasedness property remains fully design-based and does not require the validity of the prediction model.

model-assisted inference

survey estimation

auxiliary data

finite population inference

machine learning

regression 

Survey Research Methods Section