Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys
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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.
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Propensity score methods can be enhanced using ML algorithms to more accurately balance the distribution of potential confounders between exposed and unexposed groups.
“Propensity score methods, which estimate the probability of an individual receiving a particular treatment or exposure based on their observed characteristics, can be enhanced using ML algorithms to more accurately balance the distribution of potential confounders between exposed and unexposed groups [35].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [35]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Wickham, H. (2016) · Use R! · doi:10.1007/978-3-319-24277-4 · Cited by 33,612 · see citing works
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
(1992) · Cited by 23,431 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053 · see citing works
An Introduction to Categorical Data Analysis
Agresti, A. (2006) · Wiley series in probability and statistics · doi:10.1002/0470114754 · Cited by 6,697 · see citing works
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667 · see citing works
(1996) · Cited by 4,738 · see citing works
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403 · see citing works
van der Laan, M. J., Polley, E., & Hubbard, A. (2007) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1309 · Cited by 1,472 · see citing works
A tutorial on propensity score estimation for multiple treatments using generalized boosted models
McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013) · Statistics in Medicine · doi:10.1002/sim.5753 · Cited by 1,450 · see citing works
Data Preprocessing in Data Mining
García, S., Luengo, J., & Herrera, F. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10247-4 · Cited by 1,390 · see citing works
McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.403 · Cited by 1,317 · see citing works
Bethlehem, J. (2010) · International Statistical Review · doi:10.1111/j.1751-5823.2010.00112.x · Cited by 1,315 · cited 1× · see citing works
“This population was used originally by [44], following an experiment by [5] to measure the efficiency of adjustments in selection bias.”
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961 · see citing works
Improving propensity score weighting using machine learning
Lee, B. K., Lessler, J., & Stuart, E. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3782 · Cited by 888 · see citing works
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802 · see citing works
Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien
MEYER, D. A., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2015) · Cited by 523 · see citing works
Westreich, D. J., Lessler, J., & Funk, M. J. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.11.020 · Cited by 505 · see citing works
Open-source machine learning: R meets Weka
Hornik, K., Buchta, C., & Zeileis, A. (2008) · Computational Statistics · doi:10.1007/s00180-008-0119-7 · Cited by 388 · see citing works
Inference for Nonprobability Samples
Elliott, M. R., & Valliant, R. (2017) · Statistical Science · doi:10.1214/16-sts598 · Cited by 331 · see citing works
Noncoverage and nonresponse in an Internet survey
Couper, M. P., Kapteyn, A., Schonlau, M., & Winter, J. (2005) · Social Science Research · doi:10.1016/j.ssresearch.2005.10.002 · Cited by 293 · see citing works
Evaluating uses of data mining techniques in propensity score estimation: a simulation study
Setoguchi, S., Schneeweiß, S., Brookhart, M. A., Glynn, R. J., & Cook, E. F. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1555 · Cited by 273 · see citing works
Practical Tools for Designing and Weighting Survey Samples
Valliant, R. L., Dever, J. A., & Kreuter, F. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6449-5 · Cited by 224 · see citing works
Lee, S., & Valliant, R. L. (2009) · Sociological Methods & Research · doi:10.1177/0049124108329643 · Cited by 203 · see citing works
Practical Tools for Designing and Weighting Survey Samples
Valliant, R. L., Dever, J. A., & Kreuter, F. (2018) · Statistics for social and behavioral sciences · doi:10.1007/978-3-319-93632-1 · Cited by 198 · see citing works
Improving propensity score estimators’ robustness to model misspecification using super learner.
Pirracchio, R., Petersen, M. L., & van der Laan, M. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu253 · Cited by 186 · see citing works
Propensity score adjustment as a weighting scheme for volunteer panel web surveys
Lee, S.-H. (2006) · Journal of Official Statistics · Cited by 183 · see citing works
Estimating Propensity Adjustments for Volunteer Web Surveys
Valliant, R. L., & Dever, J. A. (2011) · Sociological Methods & Research · doi:10.1177/0049124110392533 · Cited by 167 · see citing works
Taylor, H. (2000) · International Journal of Market Research · doi:10.1177/147078530004200104 · Cited by 153 · see citing works
Model-Assisted Survey Estimation with Modern Prediction Techniques
Breidt, F. J., & Opsomer, J. D. (2017) · Statistical Science · doi:10.1214/16-sts589 · Cited by 151 · see citing works
Wyss, R., Ellis, A. R., Brookhart, M. A., Girman, C. J., Funk, M. J., LoCasale, R. J., & Stürmer, T. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu181 · Cited by 124 · see citing works
MEYER, D. A., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2020) · Cited by 120 · see citing works
Baffetta, F., Fattorini, L., Franceschi, S., & Corona, P. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.06.014 · Cited by 103 · see citing works
Options for Conducting Web Surveys
Schonlau, M., & Couper, M. P. (2017) · Statistical Science · doi:10.1214/16-sts597 · Cited by 95 · see citing works
Internet Surveys: Can Statistical Adjustments Eliminate Coverage Bias?
Dever, J. A., Rafferty, A. P., & Valliant, R. L. (2007) · Survey research methods · doi:10.18148/srm/2008.v2i2.128 · Cited by 91 · see citing works
Recursive Partitioning and Regression Trees [R package rpart version 4.1-15]
Therneau, T. M., & Atkinson, B. (2019) · Cited by 87 · see citing works
Marijuana use and depression among adults: testing for causal associations
Harder, V. S., Morral, A. R., & Arkes, J. A. (2006) · Addiction · doi:10.1111/j.1360-0443.2006.01545.x · Cited by 84 · see citing works
Comparing Inference Methods for Non‐probability Samples
Buelens, B., Burger, J., & van den Brakel, J. (2018) · International Statistical Review · doi:10.1111/insr.12253 · Cited by 79 · see citing works
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn184 · Cited by 76 · see citing works
Propensity score and proximity matching using random forest
Zhao, P., Su, X., Ge, T., & Fan, J. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.12.012 · Cited by 65 · see citing works
Generalized Boosted Regression Models [R package gbm version 2.1.8]
Greenwell, B., Boehmke, B. C., Cunningham, J., & Developers, G. (2020) · Cited by 62 · see citing works
Terhanian, G., Siegal, J. W., Overmeyer, C. B., Bremer, J., & Taylor, H. (2001) · International Journal of Market Research · doi:10.1177/147078530104300203 · Cited by 56 · see citing works
Miscellaneous Functions for "Grid" Graphics [R package gridExtra version 2.3]
Auguié, B. (2017) · Cited by 52 · see citing works
Austin, P. C. (2012) · Multivariate Behavioral Research · doi:10.1080/00273171.2012.640600 · Cited by 51 · see citing works
Phipps, P., & Toth, D. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas521 · Cited by 45 · see citing works
Kolenikov, S., & Buskirk, T. D. (2015) · Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · doi:10.13094/smif-2015-00003 · Cited by 35 · see citing works
An Empirical Comparison of Tree‐Based Methods for Propensity Score Estimation
Watkins, S., Jonsson‐Funk, M. L., Brookhart, M. A., Rosenberg, S. A., O’Shea, T. M., & Daniels, J. L. (2013) · Health Services Research · doi:10.1111/1475-6773.12068 · Cited by 33 · see citing works
Properties of Endogenous Post-Stratified Estimation using remote sensing data
Tipton, J., Opsomer, J. D., & Moisen, G. G. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.07.035 · Cited by 31 · see citing works
Using classification tree analysis to generate propensity score weights
Linden, A., & Yarnold, P. R. (2017) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12744 · Cited by 26 · see citing works
ColorBrewer Palettes [R package RColorBrewer version 1.1-2]
Neuwirth, E. (2014) · Cited by 24 · see citing works
A kernel smoothing method of adjusting for unit non‐response in sample surveys
da Silva, D. N., & Opsomer, J. D. (2006) · Canadian Journal of Statistics · doi:10.1002/cjs.5550340402 · Cited by 22 · see citing works
Ferri‐García, R., & Rueda, M. D. M. (2018) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.2436/20.8080.02.73 · Cited by 17 · see citing works
Self-citationRead, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files [R package xlsx version 0.6.5]
Drǎgulescu, A. A., & Arendt, C. (2020) · Cited by 15 · see citing works
C5.0 Decision Trees and Rule-Based Models [R package C50 version 0.1.5]
Kühn, M., & Quinlan, R. (2021) · Cited by 12 · see citing works
Bagging non-differentiable estimators in complex surveys
Wang, J. C., Opsomer, J. D., & Wang, H. (2014) · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [32]Montanari GE and Ranalli MG. Multiple and ridge model calibration. In: Proceedings of Workshop on Calibration and Estimation in Surveys, Ottawa, Canada, October 31-November 1 2007.
- [37]David M, Little RJA, Samuhel ME and Triest RK. Nonrandom nonresponse models based on the propensity to respond In: Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 1983, pp. 168– 173.
- [38]Da Silva DN and Opsomer JD. Nonparametric propensity weighting for survey nonresponse through local polynomial regression. Surv Methodol 2009; 35(2): 165– 176.
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“…score methods, which estimate the probability of an individual receiving a particular treatment or exposure based on their observed characteristics, can be enhanced using ML algorithms to more accurately balance the distribution of potential confounders between exposed and unexposed groups [35].”
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Yoda, F. S., & Freire, O. B. D. L. (2024) · ReMark - Revista Brasileira de Marketing · doi:10.5585/remark.v23i4.25692
Hoffmann, M., Santos, F. G., & Mercea, D. (2025) · International Journal of Sociology · doi:10.1080/00207659.2025.2458419
Lee, Y., & Leite, W. L. (2026) · Behavior Research Methods · doi:10.3758/s13428-026-03024-x
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- Authors
- Ferri-García, R., & Rueda, M. D. M.
- Affiliations
- Universidad de Granada (ES)
- Corresponding author
- Rueda, María del Mar
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-04-22
- Volume
- 15
- Issue
- 4
- Pages
- e0231500
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7176094
- OpenAlex
- W3016418892
- Cited by
- 67
- References
- 62
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 15 · 2023: 11 · 2022: 11 · 2021: 10 · 2020: 4
- FWCI
- 12.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Ministerio de Economía, Industria y Competitividad, Gobierno de España (MTM2015-63609-R)
- Ministerio de Ciencia Tecnología y Telecomunicaciones (FPU)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- propensity score adjustment, selection bias, online surveys, non-probability samples, machine learning classification, propensity score estimation, logistic regression, reweighting, self-selection, data dimensionality
- MeSH terms
- Machine Learning, Algorithms, Computer Simulation, Data Interpretation, Statistical, Humans, Surveys and Questionnaires, Research Design, Selection Bias, Logistic Models, Propensity Score
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