The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
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“Nearly all analyses show Rubin's B<25%, Rubin's R of 0.5–2, and a percentage bias of <10% for each covariate (Fig. 1) (Morgan, 2018; Rubin, 2001).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Rubin, 2001
In the citing paper
“We used directed acyclic graphs to identify factors that could be associated with both reading for pleasure and children's strengths and difficulties, or with children's strengths and difficulties only based on previous empirical research (Brookhart et al., 2006; Caliendo and Kopeinig, 2008; Rubin, 2001; Sauer and VanderWeele, 2013).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Rubin, 2001
In the citing paper
“Secondly, this process of variable selection can be based on previous research [59].”
Citations
Cites
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
Self-citationRosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Self-citation(1976) · Annals of Economic and Social Measurement · Cited by 5,035
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478078 · Cited by 3,082
Self-citationA Generalization of Sampling Without Replacement From a Finite Universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.2307/2280784 · Cited by 3,011
Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H., & Wahba, S. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473858 · Cited by 2,395
Card, D., & Krueger, A. B. (2000) · American Economic Review · doi:10.1257/aer.90.5.1397 · Cited by 2,228
Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania
Card, D., & Krueger, A. B. (1993) · National Bureau of Economic Research · doi:10.3386/w4509 · Cited by 2,072
The role of the propensity score in estimating dose-response functions
Imbens, G. W. (2000) · Biometrika · doi:10.1093/biomet/87.3.706 · Cited by 1,779
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Research works printed but not linked: 10
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Items 101–200 of 1,001
Filippatos, G. S., Ahmed, M. I., Gladden, J. D., Mujib, M. U., Aban, I., Love, T. E., Sanders, P. W., Pitt, B., Anker, S. D., & Ahmed, A. (2011) · European Heart Journal · doi:10.1093/eurheartj/ehq473 · Cited by 146
“Pulmonary oedema by chest X-ray 113 (9) 184 (13) 0.”
BackgroundA practical guide to propensity score analysis for applied clinical research
Lee, J., & Little, T. D. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.01.005 · Cited by 149
William, W., Anselmi, L., Kristensen, S. R., Lau, Y., Bailey, S. F., Bower, P., Checkland, K., Elvey, R., Rothwell, K., Stokes, J., et al. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1002113 · Cited by 147
“score, recommending that the absolute standardised difference of the means of the linear index of the propensity score between the comparator and intervention (treated) group is below 25% [29].”
BackgroundMethodInfluentialImplications of Small Samples for Generalization: Adjustments and Rules of Thumb
Tipton, E., Hallberg, K., Hedges, L. V., & Chan, W. (2016) · Evaluation Review · doi:10.1177/0193841x16655665 · Cited by 178
“With large samples, the benchmark then is a rule of thumb indicating bounds within which regression adjustments will perform well (Rubin, 2001).”
BackgroundMethodInfluentialRevascularization in Patients With Multivessel Coronary Artery Disease and Chronic Kidney Disease
Bangalore, S., Guo, Y., Samadashvili, Z., Blecker, S., Xu, J., & Hannan, E. L. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.06.1334 · Cited by 141
“CABG), given a set of baseline-measured covariates (7,8).”
BackgroundEstimating causal effects for multivalued treatments: a comparison of approaches
Linden, A., Uysal, S. D., Ryan, A. M., & Adams, J. (2015) · Statistics in Medicine · doi:10.1002/sim.6768 · Cited by 141
The Impact of Migration and Remittances on Wealth Accumulation and Distribution in Rural Thailand
Garip, F. (2013) · Demography · doi:10.1007/s13524-013-0260-y · Cited by 140
“In all six cases, the standardized difference in the matched sample is much smaller than 50 percent, the upper bound suggested by Rubin (2001) for the regression adjustment to be reliable.”
BackgroundKildea, S. V., Gao, Y., Hickey, S. D., Nelson, C., Kruske, S., Carson, A., Currie, J., Reynolds, M., Wilson, K., Watego, K., et al. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00061-9 · Cited by 139
U.S. Political Corruption and Audit Fees
Jha, A., Kulchania, M., & Smith, J. D. (2020) · The Accounting Review · doi:10.2308/tar-2017-0331 · Cited by 132
Systolic Blood Pressure and Outcomes in Patients With Heart Failure With Preserved Ejection Fraction
Tsimploulis, A., Lam, P. H., Arundel, C., Singh, S. N., Morgan, C. J., Faselis, C. J., DEEDWANIA, P. C., Butler, J., Aronow, W. S., Yancy, C. W., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2017.5365 · Cited by 133
“Heart failure readmission or all-cause mortality 172 (19) 104 (12) 1.”
BackgroundHow Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations
Tipton, E. (2014) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998614558486 · Cited by 141
“Interestingly, this rule of thumb corresponds roughly results from Rubin (2001), which suggest that balance is achieved when |d| .25 and ½ ω2 2 (i.e., b > .96).”
BackgroundMethodResultQuantifying the rebound effects of residential solar panel adoption
Qiu, Y. L., Kahn, M. E., & Xing, B. (2019) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2019.06.003 · Cited by 139
Kidney impairment in primary aldosteronism
Wu, V., Yang, S., Lin, J., Cheng, B., Kuo, C., Tsai, C., Chu, T., Huang, K., Wang, S., Lin, Y., et al. (2011) · Clinica Chimica Acta · doi:10.1016/j.cca.2011.02.018 · Cited by 133
The Use of Self-Generated Identification Codes in Longitudinal Research
Yurek, L. A., Vasey, J. J., & Havens, D. S. (2008) · Evaluation Review · doi:10.1177/0193841x08316676 · Cited by 133
“The propensity score summarized the vector of covariates previously analyzed as separate variables and accounted for the differences in the joint distribution of subject characteristics across both membership groups (Adamina et al. 2006; Rubin 2001).”
MethodMatchThem:: Matching and Weighting after Multiple Imputation
Pishgar, F., Greifer, N., Leyrat, C., & Stuart, E. A. (2021) · The R Journal · doi:10.32614/rj-2021-073 · Cited by 147
“Although some recommend avoiding the inclusion of the outcome variable during or prior to matching and weighting with propensity scores, (Rubin, 2001) statistical evidence favors the use of the outcome variable in multiple imputation of covariates (Leyrat et al., 2019).”
MethodExternal Validity in Policy Evaluations That Choose Sites Purposively
Olsen, R. B., Orr, L. L., Bell, S. H., & Stuart, E. A. (2012) · Journal of Policy Analysis and Management · doi:10.1002/pam.21660 · Cited by 164
Shadish, W. R. (2010) · Psychological Methods · doi:10.1037/a0015916 · Cited by 130
“Good propensity scores can be used to create balance over conditions on all observed covariates used to create the propensity scores (e.g., Rubin, 2001), but this is not sufficient for bias reduction unless the strong ignorability assumption is also met.”
BackgroundPropensity scores in intensive care and anaesthesiology literature: a systematic review
Gayat, É., Pirracchio, R., Resche‐Rigon, M., Mebazaa, A., Mary, J., & Porcher, R. (2010) · Intensive Care Medicine · doi:10.1007/s00134-010-1991-5 · Cited by 139
Kot, F. C. (2014) · Research in Higher Education · doi:10.1007/s11162-013-9325-4 · Cited by 154
Green, K. M., & Stuart, E. A. (2014) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0036515 · Cited by 132
“As demonstrated in Stuart and Green (2008), we use the full matching sets to form weights, which are then used in the outcome analyses.”
MethodRisks and burdens of incident dyslipidaemia in long COVID: a cohort study
Xu, E., Xie, Y., & Al‐Aly, Z. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00355-2 · Cited by 130
The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"
Ward, A. C. (2009) · Epidemiologic Perspectives & Innovations · doi:10.1186/1742-5573-6-2 · Cited by 120
“More recently, propensity score estimation (using regression as part of the process, but with no attempt to interpret regression coefficients causally) and matching has emerged as a method to warrant claims about average causal effects and average causal effects on the treated [49,55,56].”
Bullying Explains Only Part of LGBTQ–Heterosexual Risk Disparities
Robinson, J. P., & Espelage, D. L. (2012) · Educational Researcher · doi:10.3102/0013189x12457023 · Cited by 147
“…interest can be susceptible to bias if the unadjusted covariate differences between LGBTQ- and heterosexual-identified students are moderately large (Rubin, 2001).5 Moreover, Models 3 and 4 base their estimation in part on extrapolating relationships to regions of data where there exist only…”
BackgroundWang, C., Li, H., Chen, W.-C., Lu, N. T., Tiwari, R. C., Xu, Y., & Yue, L. Q. (2019) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2019.1657133 · Cited by 126
“…inference in observational studies, and can improve treatment comparison by adjusting for a relatively large number of potentially confounding covariates (Austin 2011; D’Agostino and Rubin 2000; Lunceford and Davidian 2004; Rosenbaum and Rubin 1983, 1984; Rubin 1997, 2001, 2007, 2008; Stuart 2010).”
BackgroundMethodThe Selection of Operative Versus Nonoperative Treatment in Patients With Adult Scoliosis
Glassman, S. D., Schwab, F., Bridwell, K. H., Ondra, S. L., Berven, S., & Lenke, L. G. (2007) · Spine · doi:10.1097/01.brs.0000251022.18847.77 · Cited by 121
Developmental Trends in Peer Victimization and Emotional Distress in LGB and Heterosexual Youth
Robinson, J. P., Espelage, D. L., & Rivers, I. (2013) · PEDIATRICS · doi:10.1542/peds.2012-2595 · Cited by 133
Ahmed, A., Pitt, B., Rahimtoola, S. H., Waagstein, F., White, M., Love, T. E., & D, E. B. M. (2007) · International Journal of Cardiology · doi:10.1016/j.ijcard.2006.12.001 · Cited by 119
Bennett, P., & Lutz, A. C. (2009) · Sociology of Education · doi:10.1177/003804070908200104 · Cited by 131
Revictimization After Adolescent Dating Violence in a Matched, National Sample of Youth
Exner‐Cortens, D., Eckenrode, J. J., Bunge, J., & Rothman, E. F. (2017) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2016.09.015 · Cited by 143
Effectiveness of a REDD+ Project in Reducing Deforestation in the Brazilian Amazon
Simonet, G., Subervie, J., Ezzine‐de‐Blas, D., Cromberg, M., & Duchelle, A. E. (2018) · American Journal of Agricultural Economics · doi:10.1093/ajae/aay028 · Cited by 132
“The normalized difference is considered negligible when it is below the suggested rule of thumb of 0.25 standard deviations (Rubin 2001; Imbens and Wooldridge 2009).”
BackgroundInfluentialSanchís, J. M., Bueno, H., Miñana, G., Guerrero, C., Martí, D., Martínez‐Sellés, M., Domínguez-Pérez, L., Díez‐Villanueva, P., Barrabés, J. A., Marín, F. R., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.0047 · Cited by 121
Color and credit: Race, regulation, and the quality of financial services
Begley, T. A., & Purnanandam, A. K. (2021) · Journal of Financial Economics · doi:10.1016/j.jfineco.2021.03.001 · Cited by 115
“…is not a well-developed literature on formally assessing standardized bias, each covariate falls well below the 20% threshold that Rosenbaum and Rubin (1985) call “large” and comfortably within the bounds of the balancing tests (e.g., post-match covariate variance ratio) proposed in Rubin (2001).”
BackgroundStuart, E. A. (2008) · Statistics in Medicine · doi:10.1002/sim.3207 · Cited by 119
“Second, since balance involves the comparison of the multivariate empirical distributions of the covariates, balance should be checked not only on the means of the distributions, but also on their variances and important twoor three-way interactions [2].”
BackgroundMartin, D., Glass, T. A., Bandeen‐Roche, K., Todd, A. C., Shi, W., & Schwartz, B. S. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj060 · Cited by 113
“It may also identify risk factor groups for which regression extrapolates, rather than adequately adjusts, for covariatecontrolled associations of exposures with outcomes (31) (e.”
BackgroundPack, A. I., Magalang, U. J., Singh, B., Kuna, S. T., Keenan, B. T., & Maislin, G. (2021) · Sleep · doi:10.1093/sleep/zsaa229 · Cited by 114
“A fourth strategy is to utilize analysis techniques designed to reduce bias in estimated treatment effects from observational real-world data, such as methods relying on propensity scores [79, 80].”
MethodCook, T. D., & Steiner, P. M. (2010) · Psychological Methods · doi:10.1037/a0018536 · Cited by 117
“He has worked on different matching criteria (Rubin, 2006); and more importantly, he has developed distributional criteria for indicating when treatment and comparison groups are definitely not balanced (Rubin, 2001).”
BackgroundA Case for Registering Studies of Political Outcomes: An Application in the 2010 House Elections
Monogan, J. E. (2013) · Political Analysis · doi:10.1093/pan/mps022 · Cited by 76
A Primer on Propensity Score Analysis
Shadish, W. R., & Steiner, P. M. (2010) · Newborn and Infant Nursing Reviews · doi:10.1053/j.nainr.2009.12.010 · Cited by 121
Matching With Multiple Control Groups With Adjustment for Group Differences
Stuart, E. A., & Rubin, D. B. (2007) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998607306078 · Cited by 120
“A more thorough investigation of balance should also compare other characteristics of the covariate distributions in the two groups: variances, correlations, and so on (Ho et al., 2007; Rubin, 2001).”
BackgroundInfluentialSelf-citationA matching method for improving covariate balance in cost‐effectiveness analyses
Sekhon, J. S., & Grieve, R. (2011) · Health Economics · doi:10.1002/hec.1748 · Cited by 115
West, S. G., Cham, H., Thoemmes, F. J., Renneberg, B., Schulze, J., & Weiler, M. (2014) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0036387 · Cited by 118
Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance
Brooks, J. M., & Ohsfeldt, R. L. (2012) · Health Services Research · doi:10.1111/1475-6773.12020 · Cited by 116
“…the assumption that randomized treatment assignment yields a balanced distribution of covariates thought to be related to outcome between the treatment and control groups ©Health Research and Educational Trust DOI: 10.1111/1475-6773.12020 METHODSARTICLE 1487 Health Services Research (Rubin 2001).”
BackgroundMethodInfluentialThe effectiveness of blended online learning courses at the community college level
Ryan, S., Kaufman, J. H., Greenhouse, J. B., She, R., & Shi, J. (2015) · Community College Journal of Research and Practice · doi:10.1080/10668926.2015.1044584 · Cited by 123
“Matching on the propensity score has been shown to provide more consistent estimates of the treatment effect than attempting to match on numerous individual covariates (Rubin, 2001).”
BackgroundKildea, S. V., Gao, Y., Hickey, S. D., Kruske, S., Nelson, C., Blackman, R., Tracy, S., Hurst, C. P., Williamson, D. S., & Roe, Y. L. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.06.001 · Cited by 115
“Rubin's B summarise the overall model bias and less than 25% indicate the two groups have been adequately balanced, and Rubin's R is the ratio of variances between the two groups, and a value close to 1·0 indicate adequate balance [22].”
BackgroundEstablishing Equivalence: Methodological Progress in Group-matching Design and Analysis
Kover, S. T., & Atwood, A. K. (2013) · American Journal on Intellectual and Developmental Disabilities · doi:10.1352/1944-7558-118.1.3 · Cited by 118
“Sometimes referred to as ‘‘standardized bias,’’ standardized mean differences are a simple and effective index of matching (Rosenbaum & Rubin, 1985; Rubin, 2001).”
BackgroundInfluentialCrop productivity and adaptation to climate change in Pakistan
Gorst, A., Dehlavi, A., & Groom, B. (2018) · Environment and Development Economics · doi:10.1017/s1355770x18000232 · Cited by 114
“The Rubin B and R tests also fall within the bounds suggested by Rubin (2001) to constitute sufficient balance.”
BackgroundZhang, Z., Zhang, B., & Jia, M. (2021) · The Leadership Quarterly · doi:10.1016/j.leaqua.2021.101562 · Cited by 111
Optimal full matching for survival outcomes: a method that merits more widespread use
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6602 · Cited by 115
“First, it has been shown that IPTW can be quite sensitive to extreme weights [54–56].”
Backgroundβ-Blockers and Cardiovascular Events in Patients With and Without Myocardial Infarction
Bangalore, S., Bhatt, D. L., Steg, P. G., Weber, M. A., Boden, W. E., Hamm, C. W., Montalescot, G., Hsu, A., Fox, K. A. A., & Lincoff, A. M. (2014) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.114.001073 · Cited by 109
Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies
Rubin, D. B. (2004) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986029003343 · Cited by 108
“The possible need to discard irrelevant units from the control group is emphasized using real examples (e.g., from the tobacco litigation [Rubin, 2002]).”
BackgroundMethodInfluentialSelf-citationDo Land Use Policies Follow Road Construction?Preprint
Garcia–López, M.-À., Solé‐Ollé, A., & Viladecans‐Marsal, E. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2410377 · Cited by 107
“However, it is well-known that the ability of regression analyses to adjust for differences in observed covariates is severely reduced when the between-group differences in these covariates are substantial (e.g., Rubin, 2001; Imbens and Wooldridge, 2009), as is probably the case here.”
BackgroundMethodMokhles, M. M., Körtke, H., Stierle, U., Wagner, O., Charitos, E. I., Bogers, A. J. J. C., Gummert, J. F., Sievers, H., & Takkenberg, J. J. M. (2010) · Circulation · doi:10.1161/circulationaha.110.947341 · Cited by 105
Timing of high-quality child care and cognitive, language, and preacademic development.
Li, W., Farkas, G., Duncan, G. J., Burchinal, M., & Vandell, D. L. (2012) · Developmental Psychology · doi:10.1037/a0030613 · Cited by 105
“Rubin (2001) illustrated that for the propensity score method to be trustworthy, the absolute SMD of propensity scores should be less than .25 and variance ratios should be between .5 and 2.”
Quigley, T. J., Hambrick, D. C., Misangyi, V. F., & Rizzi, G. A. (2019) · Strategic Management Journal · doi:10.1002/smj.3033 · Cited by 104
“…over the control (insiders) group, found balance across the matched groups: Rubin's B (at 17.5) and Rubin's R (at 0.77), common measures of balance, were within recommended ranges (less than 25 for B and between 0.5 and 2.0 for R) (Austin, 2009; Busenbark, Lange, & Certo, 2017; Rubin, 2001).”
BackgroundInfluentialHong, I., Goodwin, J. S., Reistetter, T., Kuo, Y., Mallinson, T., Karmarkar, A. M., Lin, Y., & Ottenbacher, K. J. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.16646 · Cited by 104
Ekundayo, O. J., Dell’Italia, L. J., Sanders, P. W., Arnett, D. K., Aban, I., Love, T. E., Filippatos, G. S., Anker, S. D., Lloyd‐Jones, D. M., Bakris, G. L., et al. (2009) · International Journal of Cardiology · doi:10.1016/j.ijcard.2009.01.010 · Cited by 104
“the conditional probability of having hyperuricemia given a set of measured baseline covariates of that participant [25,26].”
The Value of Buy‐Online‐and‐Pickup‐in‐Store in Omni‐Channel: Evidence from Customer Usage Data
Song, P., Wang, Q., Liu, H., & Li, Q. (2019) · Production and Operations Management · doi:10.1111/poms.13146 · Cited by 109
“The absolute standardized difference in means usually suggests 0.25 as the balance criteria (Rubin 2001, Stuart 2010).”
BackgroundInfluentialDo the short-term effects of a high-quality preschool program persist?
Hill, C. J., Gormley, W. T., & Adelstein, S. (2015) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2014.12.005 · Cited by 117
Grade Retention, Postsecondary Education, and Public Aid Receipt
Ou, S., & Reynolds, A. J. (2010) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373709354334 · Cited by 100
Berntsen, G., Dalbakk, M., Hurley, J. S., Bergmo, T. S., Solbakken, B., Spansvoll, L., Bellika, J. G., Skrøvseth, S. O., Brattland, T., & Rumpsfeld, M. (2019) · BMC Health Services Research · doi:10.1186/s12913-019-4397-2 · Cited by 114
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Keith, K. A., Jensen, D. D., & O’Connor, B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.474 · Cited by 127
Maiti, A., Qiao, W., Sasaki, K., Ravandi, F., Kadia, T. M., Jabbour, E. J., Daver, N. G., Borthakur, G. M., Garcia‐Manero, G., Pierce, S. A., et al. (2020) · American Journal of Hematology · doi:10.1002/ajh.26061 · Cited by 100
“25 would suggest a substantial reduction of bias between the two intervention groups.(23) A 1:1 matching was used for the overall comparison (85 patients in each group) and low TRM risk group (61 patients in each group).”
MethodAssessing the Effectiveness of a College Freshman Seminar Using Propensity Score Adjustments
Clark, M. H., & Cundiff, N. L. (2011) · Research in Higher Education · doi:10.1007/s11162-010-9208-x · Cited by 99
“While the predicted probabilities that result from a logistic regression are commonly used as propensity scores, Rubin (2001) suggests that using a logit transformation often results in better propensity scores.”
MethodBanach, M., Bhatia, V., Feller, M. A., Mujib, M. U., Desai, R. V., Ahmed, M. I., Guichard, J. L., Aban, I., Love, T. E., Aronow, W. S., et al. (2011) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2010.12.020 · Cited by 100
Skills-Displacing Technological Change and its Impact on Jobs: Challenging Technological Alarmism?
McGuinness, S., Pouliakas, K., & Redmond, P. H. (2019) · Economics of Innovation and New Technology · doi:10.1080/10438599.2021.1919517 · Cited by 105
Adamopoulos, C., Meyer, P., Desai, R. V., Karatzidou, K., Ovalle, F., White, M., Aban, I., Love, T. E., DEEDWANIA, P. C., Anker, S. D., et al. (2010) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfq159 · Cited by 99
“being obese) given a set of measured baseline characteristics.(15,16) Propensity-score matching makes it possible to design observational studies like randomized clinical trials in several key ways.”
BackgroundAmin, A. P., Pinto, D. S., House, J. A., Rao, S. V., Spertus, J. A., Cohen, M. G., Pancholy, S. B., Salisbury, A. C., Mamas, M. A., Frogge, N., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.3029 · Cited by 98
Jian, L., Yang, S., Ba, S., Lu, L., & Jiang, L. (2019) · MIS Quarterly · doi:10.25300/misq/2019/13649 · Cited by 101
“…(difference in treatment group means divided by the standard deviation of the treated) for Prize, FastTrack, PayRate, and MeanReviewRate were -0.02, 0.01, 0.02, and 0.02 for PSM, and 0.03, 0.01, 0.003, -0.01 for NNM respectively, all below the maximum acceptable distance of 0.1 (Rubin, 2001).”
BackgroundInfluentialM&A and R&D: Asymmetric Effects on Acquirers and Targets?
Szücs, F. (2012) · Research Policy · doi:10.1016/j.respol.2014.03.007 · Cited by 103
The Dynamic Effects of Tax Audits
Advani, A., Elming, W., & Shaw, J. (2021) · The Review of Economics and Statistics · doi:10.1162/rest_a_01101 · Cited by 96
“We therefore include controls for presence in the data in the years before audit.12 Table 3 shows that once we condition on past survival, the sample is balanced. the average of the sample variances in the treated and control groups (see Rosenbaum & Rubin, 1985).”
BackgroundMethodFung, K. W., Baye, F. M., Baik, S. H., Zheng, Z., & McDonald, C. J. (2023) · PLoS Medicine · doi:10.1371/journal.pmed.1004194 · Cited by 105
“Due to the large sample size, most differences between cases and controls were statistically significant, but the absolute standardized difference <0.25 indicated good balance between them (S4 Table) [43].”
BackgroundInfluentialRemittances and Household Expenditure in Nepal: Evidence from Cross-Section Data
Thapa, S., & Acharya, S. (2017) · Economies · doi:10.3390/economies5020016 · Cited by 95
Using propensity scores to estimate effects of treatment initiation decisions: State of the science
Webster‐Clark, M., Stürmer, T., Wang, T., Man, K. K. C., Marinac‐Dabic, D., Rothman, K. J., Ellis, A. R., Gokhale, M., Lunt, M., Girman, C. J., et al. (2020) · Statistics in Medicine · doi:10.1002/sim.8866 · Cited by 101
The effects of the Nepal community forestry program on biodiversity conservation and carbon storage
Luintel, H., BLUFFSTONE, R. A., & Scheller, R. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199526 · Cited by 98
Cultural engagement and mental health: Does socio-economic status explain the association?
Fancourt, D., & Steptoe, A. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112425 · Cited by 98
“(Morgan, 2018; Rubin, 2001).”
Ahmed, A. (2006) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2006.08.065 · Cited by 94
Adjustments and their Consequences-Collapsibility Analysis using Graphical Models
Greenland, S., & Pearl, J. (2011) · International Statistical Review · doi:10.1111/j.1751-5823.2011.00158.x · Cited by 91
“Nonetheless, some authors recommend selecting all variables that influence exposure X for the latter adjustments, without regard to their relation to the outcome Y (e.g., Hirano and Imbens, 2001; Rubin, 2002, 2009).”
BackgroundSchwartz, B., Cohen, Z. D., Rubel, J. A., Zimmermann, D., Wittmann, W. W., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1769219 · Cited by 101
Trani, J., Moodley, J., Anand, P., Graham, L., & Maw, M. T. T. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113449 · Cited by 103
“2 and 3) (Rubin 2001).”
Matching and Propensity Scores
Steiner, P. M., & Cook, D. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199934874.013.0013 · Cited by 96
“The standardized mean difference in covariate means, also called Cohen's d, is probably the most popular criterion (Rosenbaum & Rubin, 1985; Rubin, 2001).”
BackgroundMethodUnconditional Prenatal Income Supplement and Birth Outcomes
Brownell, M. D., Chartier, M., Nickel, N. C., Chateau, D. G., Martens, P. J., Sarkar, J., Burland, E., Jutte, D. P., Taylor, C. R., Santos, R. G., et al. (2016) · PEDIATRICS · doi:10.1542/peds.2015-2992 · Cited by 94
Covariate selection strategies for causal inference: Classification and comparison
Witte, J., & Didelez, V. (2018) · Biometrical Journal · doi:10.1002/bimj.201700294 · Cited by 88
“A main advantage is that selection is clearly separated from any outcome modeling or treatment effect estimation (Rubin, 2001).”
BackgroundResultTerrorist attacks and Europeans’ attitudes towards immigrants: An experimental approach
Ferrín, M., Mancosu, M., & Cappiali, T. M. (2019) · European Journal of Political Research · doi:10.1111/1475-6765.12362 · Cited by 91
Vascular Closure Device Failure: Frequency and Implications
Bangalore, S., Arora, N., & Resnic, F. S. (2009) · Circulation Cardiovascular Interventions · doi:10.1161/circinterventions.109.877407 · Cited by 91
“Myocardial infarction 1557 (16) 54 (20) 0.”
Winning the oil lottery: the impact of natural resource extraction on growth
Cavalcanti, T. V. D. V., Mata, D. D., & Toscani, F. G. (2019) · Journal of Economic Growth · doi:10.1007/s10887-018-09161-z · Cited by 91
On a Class of Bias-Amplifying Variables that Endanger Effect EstimatesPreprint
Pearl, J. (2010) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.1203.3503 · Cited by 188
“More generally, we will show that the prevailing over-emphasis on modeling the “treatment assignment” while disregarding the outcome (Rubin, 2002) is misguided; modeling the “outcome mechanism” is in fact a much safer strategy for estimating causal effects in observational studies.”
The effect of rural hospital closures on emergency medical service response and transport times
Miller, K. E. M., James, H. J., Holmes, M., & Houtven, C. H. V. (2020) · Health Services Research · doi:10.1111/1475-6773.13254 · Cited by 99
Heerspink, H. J. L., Kohan, D. E., & de Zeeuw, D. (2020) · Kidney International · doi:10.1016/j.kint.2020.09.026 · Cited by 89
Ribas, L. G. D. S., Pressey, R. L., Loyola, R. A., & Bini, L. M. (2020) · Biological Conservation · doi:10.1016/j.biocon.2020.108595 · Cited by 92
Endovascular Versus Open Revascularization for Peripheral Arterial Disease
Wiseman, J. T., Fernandes‐Taylor, S., Saha, S. A., Havlena, J. A., Rathouz, P. J., Smith, M. A., & Kent, K. C. (2016) · Annals of Surgery · doi:10.1097/sla.0000000000001676 · Cited by 88
Aronow, W. S., Ahmed, M. I., Ekundayo, O. J., Allman, R. M., & Ahmed, A. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2008.08.037 · Cited by 87
How do integrated health care systems address racial and ethnic disparities in colon cancer?
Rhoads, K., Patel, M. I., Ma, Y.-F., & Schmidt, L. A. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.56.8642 · Cited by 77
“Before and after match plots (see supplemental material) show a result that is very well-balanced and compliant with recommended ranges for standardized mean differences 38,39 .”
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
Khademi, A., Lee, S., Foley, D., & Honavar, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313559 · Cited by 95
“atchIt (version 3.0.2) [17] with all parameters set to their default values unless otherwise noted: (i) Exact Matching (EM); (ii) Nearest Neighbor Matching (NNM) with propensity score [43]. Following [48], we estimated the propensity scores using the logit link and transformed them to the linear scale. Then, we ran NN…”
BackgroundMethodInfluentialKoh, E., & Testa, M. F. (2008) · Social Work Research · doi:10.1093/swr/32.2.105 · Cited by 87
Koh, E. H. (2009) · Children and Youth Services Review · doi:10.1016/j.childyouth.2009.10.010 · Cited by 87
Propensity score analysis: promise, reality and irrational exuberance
Shadish, W. R. (2012) · Journal of Experimental Criminology · doi:10.1007/s11292-012-9166-8 · Cited by 84
Associations between butane hash oil use and cannabis-related problems
Meier, M. H. (2017) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2017.06.015 · Cited by 91
Adamopoulos, C., Pitt, B., Sui, X., Love, T. E., Zannad, F. M., & Ahmed, A. (2008) · International Journal of Cardiology · doi:10.1016/j.ijcard.2008.05.006 · Cited by 86
Prinja, S., Nimesh, R., Gupta, A., Bahuguna, P., Gupta, M., & Thakur, J. S. (2017) · Tropical Medicine & International Health · doi:10.1111/tmi.12895 · Cited by 81
“Secondly, this process of variable selection can be based on previous research [59].”
BackgroundBias associated with using the estimated propensity score as a regression covariate
Hade, E. M., & Lü, B. (2013) · Statistics in Medicine · doi:10.1002/sim.5884 · Cited by 88
“When covariates are all normally distributed or near normally distributed, Rubin provided very helpful guidelines regarding when regression adjustment is ‘trustworthy’ [13].”
Metadata
- Authors
- Rubin, D. B.
- Affiliations
- Harvard University (US)
- Corresponding author
- Rubin, Donald B.
- ORCID
- Rubin, Donald B.
- Venue
- Health Services and Outcomes Research Methodology
- Published
- 2001-12
- Volume
- 2
- Issue
- 3-4
- Pages
- 169-188
- ISSN
- 1387-3741 (ISSN-L)
- 1572-9400
- OpenAlex
- W2597503617
- Cited by
- 2,228
- References
- 51
- Citations by year
- 2026: 174 · 2025: 193 · 2024: 196 · 2023: 175 · 2022: 172 · 2021: 166 · 2020: 173 · 2019: 163 · 2018: 123 · 2017: 100 · 2016: 93 · 2015: 75 · 2014: 77 · 2013: 72 · 2012: 44
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Health Services and Outcomes Research Methodology (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- propensity score, observational study design, covariate balance, matching, subclassification, weighting, diagnostic tables, treatment effect estimation, tobacco litigation, National Medical Expenditure Survey, causal inference
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