The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies

MRI lesions in SpA: a comparison with noninflammatory back pain using propensity score adjustment method

Chung, H. Y., Huang, J. X., Lee, K. H., Tsang, H. H. L., Lau, C. S., & Chan, S. C. W. (2022) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x221119250 · Cited by 3

Propensity Score Matching and Difference-In-Difference Estimator for Impact Evaluations: A Case Study from Zimbabwe

Lachaud, M. A. (2023) · Contributions to economics · doi:10.1007/978-981-99-4902-1_11 · Cited by 3

Anterior Circulation Thrombectomy in Patients With Low National Institutes of Health Stroke Scale Score: Analysis of the National Inpatient Sample

Patel, K., Taneja, K., Shu, L., Zhang, L., Yu, Y., Abdalkader, M. K., Obusan, M. B., Yaghi, S. R., Nguyen, T. N., Asdaghi, N., et al. (2024) · Stroke Vascular and Interventional Neurology · doi:10.1161/svin.123.001283 · Cited by 3

Tuning-parameter-free propensity score matching approach for causal inference under shape restriction

Liu, Y., & Qin, J. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105829 · Cited by 4

Estimating additive models with missing responses

Boente, G., & Martínez, A. M. (2015) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2013.815780 · Cited by 3

Comparison of Type I error rates and statistical power of different propensity score methods

Turley, F., Redden, D. T., Case, J. L., Katholi, C. R., Szychowski, J. M., & DuBay, D. A. (2017) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2017.1406937 · Cited by 3

Controlling for confounding via propensity score methods can result in biased estimation of the conditional AUC: A simulation study

Galadima, H. I., & McClish, D. K. (2019) · Pharmaceutical Statistics · doi:10.1002/pst.1948 · Cited by 3

The Revival of Business Groups’ Risk Sharing: Evidence from Japanese Real Estate Investment Trust Market

Mori, M., Ong, S. E., & Ooi, J. T. L. (2022) · The Journal of Real Estate Finance and Economics · doi:10.1007/s11146-021-09874-w · Cited by 3

Basic Steps in Weighting

Valliant, R. L., Dever, J. A., & Kreuter, F. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6449-5_13 · Cited by 3

Semiparametric smoothing of discrete failure time data

Patil, P. N., & Bagkavos, D. (2011) · Biometrical Journal · doi:10.1002/bimj.201100058 · Cited by 3

Emerging Paths to Literacy: Modeling Individual and Environmental Contributions to Growth in Children's Emergent Literacy Skills

Swan, D. W. (2009) · Digital Archive @ GSU · doi:10.57709/1060083 · Cited by 5

Assessing Multiple Outcomes for Women with Co-Occurring Disorders and Trauma in a Multi-Site Trial: A Propensity Score Approach

Ellis, A. R., & MORRISSEY, J. P. (2009) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-009-0204-4 · Cited by 3

Analysis of Nonintervention Studies: Technical Supplement

Aickin, M. G. (2012) · The Permanente Journal · doi:10.7812/tpp/12-046 · Cited by 3

Poverty and Land Redistribution: Quasi- Experimental Evidence from South Africa's LRAD program

Keswell, M., & Carter, M. R. (2010) · Cited by 3

SAFETY IMPACTS OF DESIGN EXCEPTIONS IN UTAH

Porter, R. J. (2012) · Cited by 3

Exogenous shocks and misattribution of responsibility for economic performance: results from survey experiments

Campello, D., & Júnior, C. Z. (2015) · Cited by 3

Statistical methods for reducing bias in web surveys

Lee, M. H. (2011) · Summit (Simon Fraser University) · Cited by 3

Score de propension : intérêts, utilisation et limites. Un guide pratique pour le clinicien

Moulis, G., & Lapeyre‐Mestre, M. (2018) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2018.02.012 · Cited by 3

Learning Causal Effect Using Machine Learning with Application to China’s Typhoon

Wu, P., Hu, Q., Tong, X., & Wu, M. (2020) · Acta Mathematicae Applicatae Sinica English Series · doi:10.1007/s10255-020-0960-1 · Cited by 3

An Evaluation of Statin Use Among Patients with Type 2 Diabetes at High Risk of Cardiovascular Events Across Multiple Health Care Systems

Ward, R. C., Weeda, E. R., Bishu, K. G., Axon, R. N., Taber, D. J., & Gebregziabher, M. (2020) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2020.26.9.1090 · Cited by 3

The Inheritance bequeathed to William G. Cochran that he willed forward and left for others to will forward again: The Limits of Observational Studies that seek to Mimic Randomized Experiments

Cook, T. D. (2015) · Observational Studies · doi:10.1353/obs.2015.0012 · Cited by 3

Propensity Score Matching and Prevention Science

Sweeten, G. (2016) · Springer eBooks · doi:10.1007/978-3-319-44124-5_17 · Cited by 3

R&D Expenditure for New Technology in Livestock Farming: Impact on GHG Reduction in Developing Countries

Spada, A., Fiore, M., Monarca, U., & Faccilongo, N. (2019) · Sustainability · doi:10.3390/su11247129 · Cited by 3

Evaluación de políticas públicas sobre poblaciones heterogéneas. ¿Pueden los órganos de control externo contribuir a su avance?

Muñoz-Repiso, J. M. C., & Collado, R. R. (2007) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 3

Tackling the Problem of Self Selection in the Integration of Different Data Collection Techniques.Preprint

Camillo, F., & D’Attoma, I. (2011) · AMS Acta (University of Bologna) · doi:10.6092/unibo/amsacta/3063 · Cited by 3

Improving Propensity Score Methods in Pharmacoepidemiology

Ali, M. S. (2014) · Utrecht University Repository (Utrecht University) · Cited by 3

Towards a methodology of traffic safety measurement and program evaluation

Katz, A. (1971) · Accident Analysis & Prevention · doi:10.1016/0001-4575(71)90017-0 · Cited by 3

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal MeasuresPreprint

Labra-Spröhnle, F., Smith, G., Ahammer, H., Postlethwaite, C., Liu, I., Teesdale‐Spittle, P. H., & Frean, M. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/420513 · Cited by 3

Fallacies, Statistical

Agresti, A. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0763 · Cited by 3

Fallacies, Statistical

Agresti, A. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0763.pub2 · Cited by 3

Propensity Scoring

Clark, M. H. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.10557-4 · Cited by 3

The impact of public lighting improvement on crimes and incivilities reduction in Chile

Cadena-Urzúa, P., García, J. G., & Montes, F. (2025) · Frontiers in Sociology · doi:10.3389/fsoc.2025.1541359 · Cited by 2

What to Observe When Assuming Selection on Observables

Quinn, K. M., Liu, G., Epstein, L., & Martin, A. D. (2025) · Political Analysis · doi:10.1017/pan.2025.10003 · Cited by 2

Learning, Motivation to Study, and Patient Care Gains from the American Board of Internal Medicine’s Longitudinal Knowledge Assessment

Gray, B. M., Chesluk, B. J., Barnhart, B. J., Reddy, S. G., Heiman, H. L., & Lipner, R. S. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09819-6 · Cited by 2

Methods Using Propensity Scores to Estimate Causal Effects in Observational Longitudinal Studies

Twabi, H. S., & Manda, S. (2025) · Emerging topics in statistics and biostatistics · doi:10.1007/978-3-031-97281-2_13 · Cited by 2

Volunteer Panel Web Surveys in Urban Planning

Lee, S.-H. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-0074-4.ch003 · Cited by 2

Chemotherapy predictors and a time-dependent chemotherapy effect in metastatic esophageal cancer

Midthun, L., Kim, S., Hendifar, A., Osipov, A., Klempner, S., Chao, J., Cho, M., Guan, M., Placencio-Hickok, V. R., Gangi, A., et al. (2022) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v14.i2.511 · Cited by 1

Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk

Rogers, P. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_14 · Cited by 2

A reanalysis: Do hog farms cause disease in North Carolina neighborhoods?

Kanankege, K. S. T., Traynor, I., & Perez, A. M. (2023) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.1052306 · Cited by 2

BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction

Ni, A., Liu, M., & Qin, L. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00138 · Cited by 2

2D score-based estimation of heterogeneous treatment effects

Ye, S., Chen, Y.-Z., & Padilla, O. H. M. (2021) · Journal of Causal Inference · doi:10.1515/jci-2022-0016 · Cited by 3

Optimal refinement of strata to balance covariates

Brumberg, K., Small, D. S., & Rosenbaum, P. R. (2024) · Biometrics · doi:10.1093/biomtc/ujae061 · Cited by 2

Do coupled subsidies increase milk productivity, land use, herd size and income? Evidence from Kosovo

Bajrami, E., Wailes, E. J., Dixon, B. L., Musliu, A., & Durand‐Morat, A. (2019) · Studies in Agricultural Economics · doi:10.7896/j.1913 · Cited by 4

Evaluación del impacto de un programa de formación sobre el tiempo de búsqueda de un empleo

Muñoz-Repiso, J. M. C., & Braza, A. S. (2010) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 2

Calibration and Other Uses of Auxiliary Data in Weighting

Valliant, R. L., Dever, J. A., & Kreuter, F. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6449-5_14 · Cited by 3

Weighting to Adjust for Non-observation Errors in Telephone Surveys

Cobben, F., Schouten, B., & Bethlehem, J. G. (2011) · Springer eBooks · doi:10.1007/978-3-642-25411-6_12 · Cited by 2

A GENERALIZATION OF ADJUSTMENT, WITH AN APPLICATION TO THE SCALING OF ESSAY SCORES

Rosenbaum, P. R. (1985) · ETS Research Report Series · doi:10.1002/j.2330-8516.1985.tb00087.x · Cited by 2

Estimating the Effects of Students’ Social Networks: Does Attending a Norm-Enforcing School Pay Off?

Carolan, B. V. (2009) · The Urban Review · doi:10.1007/s11256-009-0141-2 · Cited by 2

Lack of association between the connexin 37 C1019T gene polymorphism and coronary artery disease in a Chinese population: Meta-analysis of 2,206 subjects

Li, Y., Qian, Y., & Zhou, C. (2013) · Biomedical Reports · doi:10.3892/br.2013.90 · Cited by 2

Chapter 9: Quantification Reconsidered

Walberg, H. (1984) · Review of Research in Education · doi:10.3102/0091732x011001369 · Cited by 1

Propensity Score Analysis

Brown, S. (2012) · Advances in program evaluation · doi:10.1108/s1474-7863(2012)0000013012 · Cited by 2

Propensity Score Matching in Observational Studies with Multiple Time Points

Li, C.-L. (2011) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 2

Bayesian Propensity Score Analysis: Simulation and Case Study.

Kaplan, D. E., & Chen, C. J. S. (2011) · Society for Research on Educational Effectiveness · Cited by 2

OMITTED VARIABLES, R2, AND BIAS REDUCTION IN MATCHING HIERARCHICAL DATA: A MONTE CARLO STUDY

Wang, Q., Maier, K. S., & Houang, R. T. (2017) · Journal of Statistics Advances in Theory and Applications · doi:10.18642/jsata_7100121791 · Cited by 2

Matching Strategies for Causal Inference with Observational Data in Education

Kim, Y., Lubanski, S. A., & Steiner, P. M. (2018) · Springer eBooks · doi:10.1007/978-3-319-93539-3_9 · Cited by 2

Assessing The Impact Of Adoption Of Improved Seed Yam Technology in Cameroon

Nchinda, V. P., Hadley, D., Villano, R. A., & Morales, E. L. (2019) · The Journal of Developing Areas · doi:10.1353/jda.2020.0013 · Cited by 2

The Designed Bootstrap for Causal Inference in Big Observational Data

Zhang, Y., & Sabbaghi, A. (2021) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-021-00213-z · Cited by 2

Facility profiling under competing risks using multivariate prognostic scores: Application to kidneytransplant centers

Lee, Y., & Schaubel, D. E. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211052873 · Cited by 2

Statistical methods of indirect comparison with real-world data for survival endpoint under non-proportional hazards

Lin, Z., Zhao, D., Lin, J., Ni, A., & Lin, J. (2022) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2022.2080696 · Cited by 2

Testing for covariate balance in comparative studies

Kleyman, Y. N. (2009) · Cited by 2

Biophysics Concept Inventory Survey: An Assessment in Biophysical Undergraduate Education

Latham, D. R., Alper, J., & Sanabria, H. (2024) · The Biophysicist · doi:10.35459/tbp.2023.000256 · Cited by 2

Outlier classification performance of risk adjustment methods when profiling multiple providers

Brakenhoff, T. B., Roes, K. C. B., Moons, K. G. M., & Groenwold, R. H. H. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0510-1 · Cited by 2

Parametrization of Survival Measures (Part III) Clinical Evidences in Single Arm Studies with Endpoint of Overall Survival

Szász, A., Szigeti, G. P., & Szász, M. A. (2020) · International Journal of Clinical Medicine · doi:10.4236/ijcm.2020.116034 · Cited by 2

Ischemic/bleeding event after short dual-antiplatelet therapy in patients with high bleeding risk: Sub-analysis of the MODEL U-SES study

Hioki, H., Kozuma, K., Kinoshita, Y., Nanasato, M., Ito, Y., Yamaguchi, J., Shiode, N., Hibi, K., Tanabe, K., Ako, J., et al. (2021) · Journal of Cardiology · doi:10.1016/j.jjcc.2021.03.015 · Cited by 2

Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies

Jreich, R., & Sebastien, B. (2021) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2021.1918140 · Cited by 2

Die Messung der Durchsetzung informeller Normen im Vignetten- und Feldexperiment

Petzold, K., & Eifler, S. (2020) · Kriminalität und Gesellschaft · doi:10.1007/978-3-658-27228-9_7 · Cited by 2

Health, Mental Health and Labor Productivity: The Role of Self-Reporting BiasPreprint

Leroux, J., Rizzo, J. A., & Sickles, R. C. (2004) · RePEc: Research Papers in Economics · Cited by 2

Evaluating the Impact of Health ProgrammesPreprint

Burns, J., Keswell, M., & Thornton, R. L. (2009) · RePEc: Research Papers in Economics · Cited by 2

Do Patents Shield Disclosure or Assure Exclusivity When Transacting Technology?Preprint

de Rassenfosse, G., Palangkaraya, A., & Webster, E. M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2226729 · Cited by 2

Measuring Differences among Non-Randomized Groups

Marascuilo, L. A., & Dagenais, F. (1979) · The Journal of Experimental Education · doi:10.1080/00220973.1979.11011715 · Cited by 2

Comments on Reviews by Jagdish N. Sheth of MNA and THAID

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Evaluating the Italian training on the job contract (cfl)

Tattara, G. (2005) · Cited by 2

Design‐ and Model‐Based Analysis of Propensity Score Designs

Steiner, P. M. (2016) · Wiley series in probability and statistics · doi:10.1002/9781118947074.ch14 · Cited by 2

Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data

Kendrick, C. M. (2000) · doi:10.15760/etd.3081 · Cited by 2

Customer Acquisition, Engagement, and Retention in Online Advertising

Els, M. (2020) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 2

Estimating Treatment Effects From Nonrandomized Studies Using Subclassification on Propensity Scores

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Addressing Validity and Generalizability Concerns in Field Experiments

Riener, G., & Schneider, S. O. (2021) · AEA Randomized Controlled Trials · doi:10.1257/rct.8368-1.0 · Cited by 2

GÖZLEME DAYALI ÇALIŞMALARDA PROPENSİTY SKOR VE BİR UYGULAMA

KANDEMİR, A. Ş. (2018) · Trakya Üniversitesi sosyal bilimler dergisi/Trakya Üniversitesi Sosyal Bilimler dergisi · doi:10.26468/trakyasobed.421025 · Cited by 1

Robust Small Area Estimation for Generalization

Chan, W., & Oh, J. (2022) · The Journal of Experimental Education · doi:10.1080/00220973.2022.2088671 · Cited by 1

Covariate adjusted nonparametric methods under propensity analysis

Ye, J., & Lai, D. (2024) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2024.1357816 · Cited by 1

Comparing the Power of Low vs High-Precision Methods for Measuring E. coli in Drinking Water in Low-Resource Settings

Sosa-Moreno, A., Lee, G. O., Wu, Z., Fanny, S. A., Trueba, G. A., Cooper, P. J., Lévy, K., & Eisenberg, J. N. S. (2025) · ACS ES&T Water · doi:10.1021/acsestwater.4c01117 · Cited by 1

A causal machine learning approach for investigating learners’ mental states through electroencephalography (EEG)

Jawale, A., Prabhu, R., & Tripathy, A. K. (2025) · AI Brain and Child · doi:10.1007/s44436-025-00014-2 · Cited by 1

Propensity Score‐Based Stratified Win Ratio for Augmented Control Designs

Chen, Y., Feng, Y., Sonksen, M., Wang, T., & Song, J. J. (2026) · Statistics in Medicine · doi:10.1002/sim.70487 · Cited by 1

International student mobility, occupational outcomes, and the reproduction of social and geographical inequalities

Arcidiacono, D. L., Avola, M., & D’Agata, R. (2026) · Higher Education · doi:10.1007/s10734-026-01664-8 · Cited by 1

Longitudinal Knowledge Assessment as a driver for learning: insights from a nationwide survey of endocrinologists

Gray, B. M., Qian, J., Weng, W., Chesluk, B. J., Barnhart, B. J., Lipner, R. S., & Grubina, R. (2026) · Journal of the Endocrine Society · doi:10.1210/jendso/bvag176

Counterfactual designs for quantifying management effects on soil carbon change at multi-field scales

Polussa, A., Oldfield, E. E., Runge, T., Evans, E. K., Liu, S., Forbes, E. S., Smith, C., Potash, E., Sanderman, J., Sheffer, M., et al. (2026) · Geoderma · doi:10.1016/j.geoderma.2026.118007 · Cited by 2

Measuring and testing the interview mode effect in mixed mode surveys

Camillo, F., & D’Attoma, I. (2014) · Statistica · doi:10.6092/issn.1973-2201/4501 · Cited by 1

An Introduction to Alternative Methods in Program Impact Evaluation

Nguyen, C. V. (2006) · Journal of Economic and Social Thought · doi:10.1453/jest.v3i3.937 · Cited by 1

Determinants of quality of care and treatment initiation in Medicare disabled patients with chronic hepatitis C

Chirikov, V., Shaya, F. T., Mullins, C. D., dosReis, S., Onukwugha, E., & Howell, C. D. (2015) · Expert Review of Gastroenterology & Hepatology · doi:10.1586/17474124.2015.1095087 · Cited by 1

A Note on Partial Covariate-Adjustment and Design Considerations in Noninferiority Trials When Patient-Level Data are not Available

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Do Fixed Exchange Rates Cause Greater Integration?

Hosny, A. S. (2013) · Journal of Economic Integration · doi:10.11130/jei.2013.28.4.533 · Cited by 1

The Estimation of Psychological Genetic Differences between Classes and Races

Moran, P. A. P. (1974) · The British Journal of Psychiatry · doi:10.1192/bjp.124.3.288 · Cited by 1

Weighting for Coverage Bias in Internet Surveys

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A 20-Year Longitudinal Observational Study of Somatic Antidepressant Treatment Effectiveness

Leon, A. C., Solomon, D. A., Mueller, T. I., Endicott, J., Rice, J. P., Maser, J. D., Coryell, W. H., & Keller, M. B. (2005) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/foc.3.1.106 · Cited by 1

RURAL VS URBAN.

Baidoo, B. (2013) · Cited by 1

Design and Analysis of Observational Studies

Suresh, K., Gajendragad, M. R., & Rahman, H. (2014) · International Journal of Infertility & Fetal Medicine · doi:10.5005/jp-journals-10016-1079 · Cited by 1

Nonresponse Bias in Tourism Advertising Studies: Further Analyses

Choe, Y., & Fesenmaier, D. R. (2016) · Tourism Analysis · doi:10.3727/108354216x14559233985015 · Cited by 1

Evaluación de la presencia de confusión en algunos miembros de la familia exponencial. (Evaluating the Presence of Confounding in Some Members of the Exponential Family)

Avena, L. M. A., & Salazar-Uribe, J. C. (2015) · CIENCIA EN DESARROLLO · doi:10.19053/01217488.3654 · Cited by 1

Expanding Frontiers in Research Designs, Methods, and Measurement in Support of Evidence-Based Practice in Early Childhood Special Education

Bishop, C. D., Snyder, P., Algina, J. J., & Leite, W. L. (2016) · Springer eBooks · doi:10.1007/978-3-319-28492-7_27 · Cited by 1

Bias Reduction Rates for Latent Variable Matching versus Matching through Manifest Variables with Measurement Errors

Wang, Q., Houang, R. T., & Maier, K. S. (2017) · Interdisciplinary Education and Psychology · doi:10.31532/interdiscipeducpsychol.1.1.009 · Cited by 1

The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies

Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529685 · Cited by 455

Matching to remove bias in observational studies

Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737

Exposure Misclassification in Observational Studies: We Can Do Better

Kiri, V. A. (2014) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2014.01.00003

Using multivariate matched sampling and regression adjustment to control bias in observational studies

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Using Restriction to Minimize Bias in Observational Studies—Reply

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