Weight smoothing for nonprobability surveys

Ramón Ferri-García (), Jean-François Beaumont, Keven Bosa, Joanne Charlebois and Kenneth Chu
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Ramón Ferri-García: University of Granada
Jean-François Beaumont: Statistics Canada
Keven Bosa: Statistics Canada
Joanne Charlebois: Statistics Canada
Kenneth Chu: Statistics Canada

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2022, vol. 31, issue 3, No 3, 619-643

Abstract: Abstract Adjustment techniques to mitigate selection bias in nonprobability samples often involve modelling the propensity to participate in the nonprobability sample along with inverse propensity weighting. It is well known that procedures for estimating weights are effective if the covariates selected in the propensity model are related to both the variable of interest and the participation indicator. In most surveys, there are many variables of interest, making weight adjustments difficult to determine as a suitable weight for one variable may be unsuitable for other variables. The standard compromise is to include a large number of covariates in the propensity model but this may increase the variability of the estimates, especially when some covariates are weakly related to the variables of interest. Weight smoothing, developed for probability surveys, could be helpful in these situations. It aims to remove the variability caused by overfit propensity models by replacing the inverse propensity weights with predicted weights obtained using a smoothing model. In this article, we study weight smoothing in the nonprobability survey context, both theoretically and empirically, to understand its effectiveness at improving the efficiency of estimates.

Keywords: Nonprobability samples; Propensity score adjustment; Tree-based inverse propensity-weighted estimator; Weight smoothing; 62D05 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11749-021-00795-7

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