Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Abstract

Modern survey methods may be subject to non-observable bias, from various sources. Among online surveys, for example, selection bias is prevalent, due to the sampling mechanism commonly used, whereby participants self-select from a subgroup whose characteristics differ from those of the target population. Several techniques have been proposed to tackle this issue. One such is Propensity Score Adjustment (PSA), which is widely used and has been analysed in various studies. The usual method of estimating the propensity score is logistic regression, which requires a reference probability sample in addition to the online nonprobability sample. The predicted propensities can be used for reweighting using various estimators. However, in the online survey context, there are alternatives that might outperform logistic regression regarding propensity estimation. The aim of the present study is to determine the efficiency of some of these alternatives, involving Machine Learning (ML) classification algorithms. PSA is applied in two simulation scenarios, representing situations commonly found in online surveys, using logistic regression and ML models for propensity estimation. The results obtained show that ML algorithms remove selection bias more effectively than logistic regression when used for PSA, but that their efficacy depends largely on the selection mechanism employed and the dimensionality of the data.

Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques

“…Other applications of machine learning algorithms in PSA involve their use in nonresponse adjustments; more precisely, they have been studied using Random Forests as propensity predictors [21]. Regarding the nonprobability sampling context covered in this study, [12] presented a simulation study using decision trees, k-Nearest Neighbors, Naive Bayes, Random Forests, and a Gradient Boosting Machine that support the view given in [6] about machine learning methods being used for removing selection bias in nonprobability samples. All of those algorithms, along with Discriminant Analysis and Model Averaged Neural Networks, will be used for propensity estimation in this study.…”

“…The most popular adjustment method in nonprobability settings is propensity score adjustment (PSA) or propensity weighting. This method, firstly developed by [14], was originally intended to correct the confounding bias in the experimental design context, and it is the most widely used method in practice [2,[7][8][9][10]12,[15][16][17]. In this approach, the propensity for an individual to participate in the volunteer survey is estimated by binning the data from both samples, s r and s v , together and training a machine learning model (usually logistic regression) on the variable δ, with δ k = 1 if k ∈ s v and δ k = 0 if k ∈ s r .…”

“…The Naive Bayes classifier is simple in its reasoning, but can provide precise results in PSA under certain conditions [12]. On the other hand, predictions from Naive Bayes can turn unstable when covariates with high cardinality (e.g., numerical variables) are present, as discrete domains are required for computation of probabilities [41].…”

“…The most important methods for this case are statistical matching and propensity score adjustment (PSA). There are many works studying the properties and performance of PSA [7][8][9][10][11][12], but there is not much of a bibliography that develops statistical matching in this context.…”

“…of random quantities calculated from the non-probability sample, we cannot include theoretical properties of the estimators obtained, but their behavior is studied through simulation studies that also include several propensity score techniques. Although PSA performance was compared with linear calibration in [10] and the combination of PSA with machine learning was already studied in [12], to the best of our knowledge, this is the first time that these methodologies are compared in practice and the first time that machine learning techniques are used for estimation with statistical matching from nonprobability samples.…”