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

Weekly Frequency of Meeting the Physical Activity Guidelines and Cardiometabolic Health in Children and Adolescents

WHITE, D. A., WILLIS, E. A., Ptomey, L. T., Gorczyca, A. M., & Donnelly, J. E. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002767 · Cited by 11

A Comparison of Propensity Score Weighting Methods for Evaluating the Effects of Programs With Multiple Versions

Leite, W., Aydın, B., & Gurel, S. (2019) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1409179 · Cited by 15

Determinants of the result of new rural development program in Vietnam

Hoang, Q. V. (2020) · Journal of Economics and Development · doi:10.1108/jed-12-2019-0076 · Cited by 13

Evaluating and improving a matched comparison of antidepressants and bone density

Yu, R. (2020) · Biometrics · doi:10.1111/biom.13374 · Cited by 14

Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches

Leite, W. L., Aydın, B., & Cetin‐Berber, D. D. (2021) · Evaluation Review · doi:10.1177/0193841x211020245 · Cited by 15

Propensity Scoring in Plastic Surgery Research: An Analysis and Best Practice Guide

Chu, J. J., Shamsunder, M. G., Yin, S., Rubenstein, R., Slutsky, H. L., Fischer, J., & Nelson, J. A. (2022) · Plastic and Reconstructive Surgery, Global Open · doi:10.1097/gox.0000000000004003 · Cited by 8

Interleukin-6 C-572G gene polymorphism and coronary artery disease in Asian: a meta-analysis of 2511 subjects.

Li, Y., Zhou, C., Xu, J., Qian, Y., & Wang, X. (2015) · Europe PMC (PubMed Central) · Cited by 13

Transforming Growth Factor .BETA.1 +869T/C Gene Polymorphism and Essential Hypertension: A Meta-analysis Involving 2708 Participants in the Chinese Population

Li, Y. (2011) · Internal Medicine · doi:10.2169/internalmedicine.50.4967 · Cited by 13

Optimally combining propensity score subclasses

Rudolph, K. E., Colson, K. E., Stuart, E. A., & Ahern, J. (2016) · Statistics in Medicine · doi:10.1002/sim.7046 · Cited by 13

The number of strata in propensity score stratification for a binary outcome

Neuhäuser, M., Thielmann, M., & Ruxton, G. D. (2016) · Archives of Medical Science · doi:10.5114/aoms.2016.61813 · Cited by 13

The bibliometric quotient (BQ), or how to measure a researcher’s performance capacity: A Bayesian Poisson Rasch model

Mutz, R., & Daniel, H. (2018) · Journal of Informetrics · doi:10.1016/j.joi.2018.10.006 · Cited by 13

Constructing Matched Sets and Strata

Rosenbaum, P. (1995) · Springer series in statistics · doi:10.1007/978-1-4757-2443-1_9 · Cited by 20

Can We Make Official Statistics with Self-Selection Web Surveys?

Bethlehem, J. G. (2008) · Cited by 13

Handling Baseline Differences and Missing Items in a Longitudinal Study of HIV Risk Among Runaway Youths

Song, J., Belin, T. R., Lee, M. B., Gao, X., & Rotheram‐Borus, M. J. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020327530029 · Cited by 13

The Use of Response Propensities

Bethlehem, J. G., Cobben, F., & Schouten, B. (2011) · doi:10.1002/9780470891056.ch11 · Cited by 13

Observational Studies: Overview

Rosenbaum, P. (2001) · Elsevier eBooks · doi:10.1016/b0-08-043076-7/00483-6 · Cited by 15

Classifying radiographic progression status in early rheumatoid arthritis patients using propensity scores to adjust for baseline differences

Park, G. S., Wong, W. K., Oh, M., Khanna, D., Gold, R. H., Sharp, J. T., & Paulus, H. E. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280207070623 · Cited by 12

Propensity Score Estimates in Multilevel Models for Causal Inference

Eckardt, P. A. (2012) · Nursing Research · doi:10.1097/nnr.0b013e318253a1c4 · Cited by 12

CAPN10 SNP43 G>A gene polymorphism and type 2 diabetes mellitus in the Asian population: a meta-analysis of 9353 participants

Li, Y., Gong, G., Geng, H., Yang, Z., Zhou, C., Xu, J., Qian, Y., Wang, X., & Chen, A. (2014) · Endocrine Journal · doi:10.1507/endocrj.ej14-0297 · Cited by 12

Evaluation of psychiatric interventions in an observational study: issues in design and analysis

Leon, A. C. (2011) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2011.13.2/aleon · Cited by 12

Comparing treatments via the propensity score: stratification or modeling?

Myers, J. A., & Louis, T. A. (2012) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-012-0080-3 · Cited by 12

TGF-β1 Gene -509C/T Polymorphism and Coronary Artery Disease: An Updated Meta-Analysis Involving 11,701 Subjects

Li, Y., Zhou, Y., Gong, G., Geng, H., & Yang, X. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00108 · Cited by 12

Análisis de la causalidad desde los estudios observacionales y su aplicación en la investigación clínica en Cuidados Intensivos

Coscia, C., Muriel, A., & Peñuelas, Ó. (2018) · Medicina Intensiva · doi:10.1016/j.medin.2018.01.002 · Cited by 11

Discontinuation of biologic therapy due to lack/loss of response and adverse events is similar between TNFi and non-TNFi class: Results from a real-world rheumatoid arthritis cohort

Movahedi, M., Hepworth, E., Mirza, R. D., Cesta, A., LARCHÉ, M. J., & Bombardier, C. (2020) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2020.06.020 · Cited by 12

Cuestionarios psicológicos e investigación en Internet: una revisión de la literatura

Eiroá‐Orosa, F. J., Fernández-Pinto, I., & Sales, P. P. (2008) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 12

A New Strategy for Reducing Selection Bias in Nonexperimental Evaluations, and the Case of How Public Assistance Receipt Affects Charitable Giving

Peck, L. R., D’Attoma, I., Camillo, F., & Guo, C. (2012) · Policy Studies Journal · doi:10.1111/j.1541-0072.2012.00466.x · Cited by 13

THE BIAS DUE TO INCOMPLETE HATCHING

Rosenbaum, P. R., & Rubin, D. B. (1983) · ETS Research Report Series · doi:10.1002/j.2330-8516.1983.tb00037.x · Cited by 12

Properties of the weighting cell estimator under a nonparametric response mechanism

da Silva, D. N., & Opsomer, J. D. (2004) · Americanae (AECID Library) · Cited by 12

Reducing the Bias in Online Reviews Using Propensity Score Adjustment

Han, S., & Mikhailova, D. (2024) · Cornell Hospitality Quarterly · doi:10.1177/19389655231223364 · Cited by 12

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration — a Korean translation

Vandenbroucke, J. P., von Elm, E. B., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S., Poole, C., Schlesselman, J. J., & Egger, M. (2024) · Ewha Medical Journal · doi:10.12771/emj.2024.e31 · Cited by 12

Estimating causal effects with optimization-based methods: A review and empirical comparison

Cousineau, M., Verter, V., Murphy, S. A., & Pineau, J. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.01.046 · Cited by 11

Constrained optimization for stratified treatment rules with multiple responses of survival data

Huang, S., Wan, X., Qiu, H., Li, L., & Yu, H. (2022) · Information Sciences · doi:10.1016/j.ins.2022.02.047 · Cited by 11

The AURKA Gene rs2273535 Polymorphism Contributes to Breast Carcinoma Risk - Meta-analysis of Eleven Studies

Guo, X., Zheng, L., Feng, W., & Xia, Y. (2014) · Asian Pacific Journal of Cancer Prevention · doi:10.7314/apjcp.2014.15.16.6709 · Cited by 11

Which Propensity Score Method Best Reduces Confounder Imbalance? An Example From a Retrospective Evaluation of a Childhood Obesity Intervention

Schroeder, K., Jia, H.-M., & Smaldone, A. (2016) · Nursing Research · doi:10.1097/nnr.0000000000000187 · Cited by 8

The R package NonProbEst for estimation in non-probability surveys

Rueda, M. D. M., Ferri‐García, R., & Castro-Martín, L. (2020) · The R Journal · doi:10.32614/rj-2020-015 · Cited by 11

Risk of community-acquired pneumonia requiring hospitalization in patients with spondyloarthritis

Chung, H. Y., Tam, L., Chan, S. C. W., Cheung, J. P. Y., Wong, P. Y., Ciang, C. O., Ng, H. Y., Law, M. Y., Lai, T. L., Wong, C.-H. P., et al. (2020) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x20962618 · Cited by 11

Overt Bias in Observational Studies

Rosenbaum, P. R. (1995) · Springer series in statistics · doi:10.1007/978-1-4757-2443-1_3 · Cited by 11

Non‐response bias in internet‐based advertising conversion studies

Park, S., & Fesenmaier, D. R. (2012) · International Journal of Culture Tourism and Hospitality Research · doi:10.1108/17506181211265077 · Cited by 11

TAP1 I333V gene polymorphism and type 1 diabetes mellitus: a meta‐analysis of 2248 cases

Li, Y., Gao, W. Q., Pang, S., Min, X., Yang, Z., Wang, H., Lu, X., Wang, L., Wang, X., Qian, Y., et al. (2014) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.12244 · Cited by 11

Association Between Rs3807989 Polymorphism in Caveolin-1 (CAV1) Gene and Atrial Fibrillation: A Meta-Analysis

Jia, W., Qi, X., & Li, Q. (2016) · Medical Science Monitor · doi:10.12659/msm.896826 · Cited by 11

ROLE OF FIRST‐YEAR MATERNAL EMPLOYMENT AND PATERNAL INVOLVEMENT IN BEHAVIORAL AND COGNITIVE DEVELOPMENT OF YOUNG CHILDREN

Im, Y., & VanderWeele, T. J. (2018) · Infant Mental Health Journal · doi:10.1002/imhj.21716 · Cited by 11

The Effect of Physical Activity Bout Patterns on Metabolic Syndrome Risk Factors in Youth: National Health and Nutrition Examination Survey 2003–2006

White, D. A., Oh, Y., & Willis, E. A. (2018) · Journal of Physical Activity and Health · doi:10.1123/jpah.2017-0693 · Cited by 11

What do We Gain? Combining Propensity Score Methods and Multilevel ModelingPreprint

Su, Y., & Cortina, J. (2009) · SSRN Electronic Journal · Cited by 11

STATISTICAL ESTIMATION METHODS IN VOLUNTEER PANEL WEB SURVEYS

Lee, S.-H. (2004) · University Libraries (University of Maryland) · Cited by 11

Propensity Score Stratification Using Multilevel Models to Examine Charter School Achievement Effects

Xiang, Y., & Tarasawa, B. (2015) · Journal of School Choice · doi:10.1080/15582159.2015.1028862 · Cited by 9

Solute Carrier Family 30 Member 8 Gene 807C/T Polymorphism and Type 2 Diabetes Mellitus in the Chinese Population: A Meta-Analysis Including 6,942 Subjects

Li, Y., Lu, X.-Z., Wang, H., Yang, X., Geng, H., Gong, G., Zhan, Y., Kim, H., & Yang, Z. (2018) · Frontiers in Endocrinology · doi:10.3389/fendo.2018.00263 · Cited by 10

Missing, Biased, and Unrepresentative: The Quantitative Analysis of Multisource Biographical Data

Cohen, G. (2002) · Historical Methods A Journal of Quantitative and Interdisciplinary History · doi:10.1080/01615440209601206 · Cited by 10

Comparison of the predictive qualities of three prognostic models of colorectal cancer

Manne, U. (2010) · Frontiers in Bioscience-Elite · doi:10.2741/e146 · Cited by 10

CD14 gene-159C/T polymorphism and coronary artery disease: a meta-analysis involving 4467 subjects.

Li, Y., Wang, X., Zhou, C., Xu, J., & Qian, Y. (2015) · Europe PMC (PubMed Central) · Cited by 10

A Propensity Scoring Approach to Characterizing the Effects of Maternal Smoking During Pregnancy on Offspring’s Initial Responses to Cigarettes and Alcohol

Bidwell, L. C., Palmer, R. H. C., Brick, L. A. D., Madden, P. A. F., Heath, A. C., & Knopik, V. S. (2016) · Behavior Genetics · doi:10.1007/s10519-016-9791-5 · Cited by 10

Type 2 diabetes mellitus and TCF7L2 gene rs12255372 G/T polymorphism: a meta-analysis involving 7990 subjects

Li, Y., Yang, X., Geng, H., & Gong, G. (2017) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-017-0560-7 · Cited by 10

Improved survival in Medicare patients with multiple myeloma: findings from a large nationwide and population-based cohort

Chen, Y., Lairson, D. R., Chan, W., & Du, X. L. (2017) · Medical Oncology · doi:10.1007/s12032-017-1001-7 · Cited by 10

Melanoma risk assessment based on relatives’ age at diagnosis

Wu, Y. P., Kohlmann, W., Curtin, K. P., Yu, Z., Hanson, H. A., Hashibe, M., Parsons, B. G., Wong, J., Schiffman, J. D., Grossman, D., et al. (2017) · Cancer Causes & Control · doi:10.1007/s10552-017-0994-8 · Cited by 10

Addressing transit mode location bias in built environment-transit mode use research

Aston, L., Currie, G., Kamruzzaman, M., Delbosc, A., Fournier, N., & Teller, D. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102786 · Cited by 10

Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys

Ferri‐García, R., & Rueda, M. D. M. (2022) · Statistical Papers · doi:10.1007/s00362-022-01296-x · Cited by 10

An introduction to propensity-score methods for reducing confounding in observational studies

Austin, P. C. (2011) · Multivariate Behavioral Research · Cited by 10

An application of propensity score methods to estimate the treatment effect of corticosteroids in patients with severe cutaneous adverse reactions

Sekula, P., Caputo, A., Dunant, A., Roujeau, J., Mockenhaupt, M., Sidoroff, A., & Schumacher, M. C. (2009) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1863 · Cited by 10

Analysis of the Effectiveness and Cost Benefit of Leukotriene Modifiers in Adults With Asthma in the Ohio Medicaid Population

Heaton, P. C., Guo, J. J., Hornung, R., Johnston, J. A., Jang, R., Moomaw, C. J., & Cluxton, R. J. (2006) · Journal of Managed Care Pharmacy · doi:10.18553/jmcp.2006.12.1.33 · Cited by 10

Approaches to risk-adjusting outcome measures applied to criminal justice involvement after community service

Banks, S. M., Pandiani, J. A., & Bramley, J. A. (2001) · The Journal of Behavioral Health Services & Research · doi:10.1007/bf02287241 · Cited by 10

Variable Importance Without Impossible Data

Mase, M., Owen, A. B., & Seiler, B. B. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040722-045325 · Cited by 13

Association between lumican gene -1554 T/C polymorphism and high myopia in Asian population: a meta-analysis.

Liao, X., Yang, X., Liao, M., Lan, C., & Liu, L. (2013) · Europe PMC (PubMed Central) · doi:10.3980/j.issn.2222-3959.2013.05.28 · Cited by 9

Analyses that Inform Policy Decisions

Gutman, R., & Rubin, D. B. (2012) · Biometrics · doi:10.1111/j.1541-0420.2011.01732.x · Cited by 9

Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

Tu, C., Jiao, S., & Koh, W. Y. (2012) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2012.685169 · Cited by 9

Comprehensive Assessment of the Association of WNK4 Polymorphisms with Hypertension: Evidence from a Meta-Analysis

Guo, X., Ding, J., Xu, H., Xuan, T., Jin, W.-Q., Yin, X., Shang, Y., Zhang, F., Zhu, J., & Zheng, L. (2014) · Scientific Reports · doi:10.1038/srep06507 · Cited by 9

Web Surveys: Methodological Problems and Research Perspectives

Biffignandi, S., & Bethlehem, J. G. (2011) · Springer eBooks · doi:10.1007/978-3-642-21037-2_33 · Cited by 9

The C8092A polymorphism in the ERCC1 gene and breast carcinoma risk: a meta-analysis of case-control studies.

Guo, X., Wang, Q., Xia, Y., & Zheng, L. (2015) · Europe PMC (PubMed Central) · Cited by 9

Estimation of conditional and marginal odds ratios using the prognostic score

Hajage, D., Rycke, Y. D., Chauvet, G., & Tubach, F. (2016) · Statistics in Medicine · doi:10.1002/sim.7170 · Cited by 10

EPCR Gene Ser219Gly Polymorphism and Venous Thromboembolism: A Meta-Analysis of 9,494 Subjects

Li, Y., Wu, J., Yang, X., Geng, H., Gong, G., & Kim, H. J. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00339 · Cited by 9

Analysis of causality from observational studies and its application in clinical research in Intensive Care Medicine

Requena, C. C., Muriel, A., & Peñuelas, Ó. (2018) · Medicina Intensiva (English Edition) · doi:10.1016/j.medine.2018.01.010 · Cited by 10

Cost-Effectiveness of White Blood Cell Growth Factor Use among a Large Nationwide Cohort of Elderly Non-Hodgkin's Lymphoma Patients Treated with Chemotherapy

Gruschkus, S. K., Lairson, D. R., Dunn, J. K., Risser, J., & Du, X. L. (2011) · Value in Health · doi:10.1016/j.jval.2010.09.010 · Cited by 9

Assessing the sensitivity of treatment effect estimates to differential follow-up rates: implications for translational research

Griffin, B. A., McCaffrey, D. F., Ramchand, R., Hunter, S. B., & Booth, M. S. (2012) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-012-0089-7 · Cited by 10

Chronic Statin Use and Long-Term Rates of Sepsis

Wang, H. E., Griffin, R. L., Shapiro, N. I., Howard, G., & Safford, M. M. (2014) · Journal of Intensive Care Medicine · doi:10.1177/0885066614550280 · Cited by 9

A comparison of mixed‐effects quantile stratification propensity adjustment strategies for longitudinal treatment effectiveness analyses of continuous outcomes

Leon, A. C., & Hedeker, D. (2006) · Statistics in Medicine · doi:10.1002/sim.2732 · Cited by 9

Dual imputation model for incomplete longitudinal data

Jolani, S., Frank, L. E., & van Buuren, S. (2013) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12021 · Cited by 9

Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

Chan, W. (2017) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998617733828 · Cited by 9

Comprehensive Review of Genetic Association Studies and Meta-Analysis on polymorphisms in microRNAs and Urological Neoplasms Risk

Zhang, L., Zhang, M., Wang, Y., Wang, Y., Zhou, J., Hao, Z., Zhang, L., & Liang, C. Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-21749-4 · Cited by 9

Female and Male Admission to Graduate School: An Illustrative Inquiry

Maxwell, S. E., & Jones, L. V. (1976) · Journal of Educational Statistics · doi:10.2307/1164733 · Cited by 9

Stratified learning: A general‐purpose statistical method for improved learning under covariate shift

Autenrieth, M., van Dyk, D. A., Trotta, R., & Stenning, D. C. (2023) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11643 · Cited by 10

Improving Meeting Inclusiveness using Speech Interruption Analysis

Fu, S., Fan, Y., Hosseinkashi, Y., Gupchup, J., & Cutler, R. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548379 · Cited by 11

The Impact of Microfinance on Household Livelihoods: Evidence from Rural Eritrea

Habte, A. T., Visser, K., & Ocran, M. K. (2017) · Springer eBooks · doi:10.1007/978-3-319-54166-2_4 · Cited by 8

Big Data vs. Clinical Trials in HPB Surgery

de Geus, S. W. L., Sachs, T. E., & Tseng, J. F. (2020) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-020-04536-3 · Cited by 9

The voice of radio in the battle for equal rights: Evidence from the U.S. South

Bernini, A. (2022) · Economics and Politics · doi:10.1111/ecpo.12213 · Cited by 8

Breast cancer distant recurrence lead time interval by detection method in an institutional cohort

Kaplan, H. G., Malmgren, J. A., & Atwood, M. K. (2020) · BMC Cancer · doi:10.1186/s12885-020-07609-3 · Cited by 8

Using Propensity Scores to Reduce Selection Bias in Mathematics Education Research

Graham, S. E. (2010) · Journal for Research in Mathematics Education · doi:10.5951/jresematheduc.41.2.0147 · Cited by 8

Sparse-data bias accompanying overly fine stratification in an analysis of beryllium exposure and lung cancer risk

Rothman, K. J., & Mosquin, P. L. (2012) · Annals of Epidemiology · doi:10.1016/j.annepidem.2012.11.005 · Cited by 8

Causal inference in randomized and non-randomized studies: The definition, identification, and estimation of causal parameters.

Sobel, M. E. (2009) · SAGE Publications Ltd eBooks · doi:10.4135/9780857020994.n1 · Cited by 10

Estimating causal associations of atopic dermatitis with depression using the propensity score method: an analysis of Korea Community Health Survey data, 2010-2013

Choi, H. M., Kim, D., Lee, W., & Kim, H. (2018) · Epidemiology and Health · doi:10.4178/epih.e2018059 · Cited by 8

Why Machines Matter for Survey and Social Science Researchers

Buskirk, T. D., & Kirchner, A. (2020) · doi:10.1002/9781118976357.ch1 · Cited by 8

How estimating nuisance parameters can reduce the variance (with consistent variance estimation)

Lok, J. J. (2024) · Statistics in Medicine · doi:10.1002/sim.10164 · Cited by 7

The G801A Polymorphism in the CXCL12 Gene and Risk of Breast Carcinoma: Evidence from a Meta-Analysis Including 2,931 Subjects

Xia, Y., Guo, X., & Ji, T. (2014) · Asian Pacific Journal of Cancer Prevention · doi:10.7314/apjcp.2014.15.6.2857 · Cited by 7

Estimation of unobservable selection effects in on-line surveys through propensity score matching: An application to public acceptance of healthy eating policies

Capacci, S., Mazzocchi, M., & Brasini, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196020 · Cited by 7

Working with Response Probabilities

Bethlehem, J. G. (2020) · Journal of Official Statistics · doi:10.2478/jos-2020-0033 · Cited by 7

FVII gene R353Q polymorphism and coronary heart disease: a meta-analysis including 3258 subjects

Li, Y., Wang, H., & Zhang, Y.-Y. (2021) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-020-02363-6 · Cited by 7

The Effects of the Revised Housing Allowance System on Housing Cost Burden of Rented Households : An Analysis Using Propensity Score Matching Techniques Combined with a Dynamic Panel Data Model

Hong, Z., & Park, I. K. (2021) · 국토계획 · doi:10.17208/jkpa.2021.04.56.2.22 · Cited by 7

Evaluation of propensity score methods for causal inference with high-dimensional covariates

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The ifo Education Survey 2014–2021: A New Dataset on Public Preferences for Education Policy in Germany

Freundl, V., Grewenig, E., Kugler, F., Lergetporer, P., Schüler, R. M., Wedel, K., Werner, K., Wirth, O., & Woessmann, L. (2022) · Jahrbücher für Nationalökonomie und Statistik · doi:10.1515/jbnst-2022-0051 · Cited by 7

Correlation Is Not Causation, Yet… Matching and Weighting for Better Counterfactuals

Negri, F. (2023) · Texts in quantitative political analysis · doi:10.1007/978-3-031-12982-7_4 · Cited by 10

Improved weak lensing photometric redshift calibration via StratLearn and hierarchical modelling

Autenrieth, M., Wright, A. H., Trotta, R., van Dyk, D. A., Stenning, D. C., & Joachimi, B. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2243 · Cited by 7

Uso de la metodología propensity score en la investigación sanitaria

Ruíz, M. E., Bailén, M. R., Pérez-Vicente, S., & Fernández, P. G. (2008) · Revista Clínica Española · doi:10.1157/13124317 · Cited by 7

Survey methods in an Age of Austerity: Driving Value in Survey Design

Williams, J. A. (2012) · International Journal of Market Research · doi:10.2501/ijmr-54-1-035-047 · Cited by 7

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

Psaty, B. M., & Siscovick, D. S. (2010) · JAMA · doi:10.1001/jama.2010.1728