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].”
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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
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A generalization of sampling without replacement from a finite universe
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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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Youth unemployment and personality traits
Mendolia, S., & Walker, I. (2015) · IZA Journal of Labor Economics · doi:10.1186/s40172-015-0035-3 · Cited by 61
“However, a number of authors, including Heckman et al. (1998), Rubin and Thomas (2000), and Rubin (2001), have shown that linear regression augmented by richer covariates can actually increase bias in the estimated treatment effect when the true relationship between the covariate and outcome is…”
Soak up the sun: Impact of solar energy systems on residential home values in Arizona
Qiu, Y. L., Wang, Y. D., & Wang, J. (2017) · Energy Economics · doi:10.1016/j.eneco.2017.07.001 · Cited by 59
Pearce, S. M., Pariser, J. J., Karrison, T., Patel, S. G., & Eggener, S. E. (2016) · The Journal of Urology · doi:10.1016/j.juro.2016.01.105 · Cited by 57
Wendel‐Garcia, P. D., Caccioppola, A., Coppola, S., Pozzi, T., Ciabattoni, A., Cenci, S., & Chiumello, D. (2021) · Critical Care · doi:10.1186/s13054-021-03578-6 · Cited by 61
Zeng, F., An, J. J., Scully, R. E., Barrington, C., Patel, B. V., & Nichol, M. B. (2010) · Value in Health · doi:10.1111/j.1524-4733.2010.00730.x · Cited by 57
Data Fusion: Identification Problems, Validity, and Multiple Imputation
Rässler, S. (2004) · Austrian Journal of Statistics · doi:10.17713/ajs.v33i1&2.436 · Cited by 57
Li, Y., Zhang, J., & Lyu, Y. (2023) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2023.04.013 · Cited by 55
Social Valuation Across Multiple Audiences: The Interplay of Ability and Identity Judgments
Fini, R., Jourdan, J., & Perkmann, M. (2017) · Academy of Management Journal · doi:10.5465/amj.2016.0661 · Cited by 61
Parish, W. J., Keyes, V., Beadles, C. A., & Kandilov, A. M. G. (2017) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-017-0174-z · Cited by 56
“To compare weighted variances one can calculate the ratio of the weighted variance among treated subjects relative to untreated subjects (Rubin 2001).”
BackgroundWang, Z. (2009) · The Internet Journal of Epidemiology · doi:10.5580/58b · Cited by 56
Economic evaluation of the anti-stigma social marketing campaign in England 2009-2011
Evans-Lacko, S., Henderson, C., Thornicroft, G., & McCrone, P. (2013) · British Journal of Psychiatry · doi:10.1192/bjp.bp.112.113746 · Cited by 55
Digoxin discontinuation and outcomes in patients with heart failure with reduced ejection fraction
Malik, A., Masson, R., Singh, S., Wu, W.-C., Packer, M., Pitt, B., Waagstein, F., Morgan, C. J., Allman, R. M., Fonarow, G. C., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.05.064 · Cited by 55
Frost, N., Zhamurashvili, T., von Laffert, M., Klauschen, F., Ruwwe‐Glösenkamp, C., Raspe, M., Brunn, M., Ochsenreither, S., Temmesfeld‐Wollbrück, B., Suttorp, N., et al. (2020) · Clinical Lung Cancer · doi:10.1016/j.cllc.2020.05.014 · Cited by 57
McCarthy, I., Robinson, C., Huq, S., Philastre, M., & Fine, R. L. (2014) · Health Services Research · doi:10.1111/1475-6773.12203 · Cited by 55
“The use of propensity score matching to limit the sample is a common technique that helps to improve balance in the covariate distribution across treatment and control groups without biasing the estimate of the treatment effect (Rubin 2001; Rubin and Thomas 1996).”
BackgroundMethodInfluentialBorghi, M., & Mariani, M. M. (2023) · Journal of Travel Research · doi:10.1177/00472875231190610 · Cited by 60
“Moreover, for the overall sample, we calculated the Rubin’s B and the Rubin’s R indicators (Rubin, 2001), which equated to 12.77 and 1.31, respectively: these values perfectly fit the intervals recommended by Rubin (2001) for considering the sample sufficiently balanced.”
MethodInfluentialDEEDWANIA, P. C., Patel, K., Fonarow, G. C., Desai, R. V., Zhang, Y., Feller, M. A., Ovalle, F., Love, T. E., Aban, I., Mujib, M. U., et al. (2013) · International Journal of Cardiology · doi:10.1016/j.ijcard.2013.05.038 · Cited by 54
Multivariate Continuous Blocking to Improve Political Science Experiments
Moore, R. T. (2012) · Political Analysis · doi:10.1093/pan/mps025 · Cited by 53
Isolated Diastolic Hypotension and Incident Heart Failure in Older Adults
Guichard, J. L., Desai, R. V., Ahmed, M. I., Mujib, M. U., Fonarow, G. C., Feller, M. A., Ekundayo, O. J., Bittner, V., Aban, I., White, M., et al. (2011) · Hypertension · doi:10.1161/hypertensionaha.111.178178 · Cited by 54
Long-term Outcomes of Endovascular and Open Repair for Traumatic Thoracic Aortic Injury
Cheng, Y., Cheng, C., Wang, S., Wu, V. C., Chu, P., Chou, A., Chen, C.-C., Ko, P., Liu, K., & Chen, S. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.7861 · Cited by 55
Jiang, Y., Ho, Y., Yan, X., & Tan, Y. (2020) · Information Systems Research · doi:10.1287/isre.2019.0909 · Cited by 54
Sadan, O., Waddel, H., Moore, R. H., Feng, C., Mei, Y., Pearce, D., Kraft, J., Pimentel, C., Mathew, S., Akbik, F., et al. (2021) · Journal of neurosurgery · doi:10.3171/2020.12.jns203673 · Cited by 54
Ahmed, A., Aban, I. B., Vaccarino, V., Lloyd‐Jones, D. M., Goff, D. C., Zhao, J., Love, T. E., Ritchie, C., Ovalle, F., Gambassi, G., et al. (2007) · Heart · doi:10.1136/hrt.2006.113522 · Cited by 54
Exporting and Productivity in the United KingdomPreprint
Kneller, R., & Greenaway, D. (2005) · SSRN Electronic Journal · doi:10.2139/ssrn.764385 · Cited by 54
“If there are substantial differences in the distribution of the observed covariates between exporters and non-exporters, as the theory and Table 4 suggest, then a pooled regression will yield unreliable results (Rubin, 2001).”
You, J. (2019) · Strategic Organization · doi:10.1177/1476127019893929 · Cited by 53
“…firms with less than three female directors), found balance across the matched groups: Rubin’s B (at 15.2) and Rubin’s R (at 0.68), 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 et al., 2017; Rubin, 2001).”
BackgroundInfluentialThe z-difference can be used to measure covariate balance in matched propensity score analyses
Kuß, O. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.06.001 · Cited by 54
Estimation of causal effects of binary treatments in unconfounded studies
Gutman, R., & Rubin, D. B. (2015) · Statistics in Medicine · doi:10.1002/sim.6532 · Cited by 52
“,X ort iL−1), with X ort il being Xil orthogonal to u(ê(Xi)) [28].”
BackgroundSelf-citationCritical Issues and Common Pitfalls in Designing and Conducting Impact Studies in Education
Song, M., & Herman, R. (2010) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373710373389 · Cited by 56
“When substantial differences in covariate distributions exist between the study groups, covariate adjustment may overcorrect or undercorrect for the initial bias, because it relies heavily on extrapolation and strong model assumptions (Cochran & Rubin, 1973; Rubin, 2001).”
BackgroundArundel, C., Lam, P. H., Khosla, R., Blackman, M. R., Fonarow, G. C., Morgan, C. J., Zeng, Q. T., Fletcher, R. D., Butler, J., Wu, W.-C., et al. (2016) · The American Journal of Medicine · doi:10.1016/j.amjmed.2016.06.018 · Cited by 54
Chervier, C., & Costedoat, S. (2017) · World Development · doi:10.1016/j.worlddev.2017.04.014 · Cited by 60
Ziff, O. J., Carter, P. R., McGowan, J., Uppal, H., CHANDRAN, S. R., Russell, S. D., Bainey, K. R., & Potluri, R. (2017) · International Journal of Cardiology · doi:10.1016/j.ijcard.2017.06.033 · Cited by 55
Biggeri, M., Burchi, F., Ciani, F., & Herrmann, R. T. (2018) · Food Policy · doi:10.1016/j.foodpol.2018.06.001 · Cited by 53
Mori-Clement, Y. (2018) · World Development · doi:10.1016/j.worlddev.2018.06.014 · Cited by 53
“If the Rubin’s variance ratio is applied, it is the ratio of the variance of the residuals orthogonal to the linear index of the propensity score in the treated group over the control group (Rubin, 2001).”
MethodZhang, C., Zhou, X., Zhou, B., & Zhao, Z. (2022) · China Economic Review · doi:10.1016/j.chieco.2022.101782 · Cited by 52
SMALL BUSINESS BORROWING AND PEER‐TO‐PEER LENDING: EVIDENCE FROM LENDING CLUB
Nowak, A., Ross, A., & Yencha, C. (2017) · Contemporary Economic Policy · doi:10.1111/coep.12252 · Cited by 52
Hersh, C. M., Love, T. E., Cohn, S., Hara-Cleaver, C., Bermel, R. A., Fox, R. J., Cohen, J. A., & Ontaneda, D. (2016) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2016.08.002 · Cited by 52
Im, M. H., Hughes, J. N., Cao, Q., & Kwok, O. (2016) · American Educational Research Journal · doi:10.3102/0002831216667479 · Cited by 71
“Specifically, using the weighted propensity scores, we calculated the absolute standardized mean difference (SMD) of the 44 covariates between participation groups (Rubin, 2001; Stuart, 2010).”
MethodSoland, J. G., & Sandilos, L. E. (2020) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1080/10824669.2020.1787171 · Cited by 80
“Changing to regressions is not meant to suggest that the models are now causal in a quasiexperimental sense (Rubin, 2001).”
BackgroundHow do Payments for Environmental Services Affect Land Tenure? Theory and Evidence From China
Liu, Z., Gong, Y., & Kontoleon, A. V. (2017) · Ecological Economics · doi:10.1016/j.ecolecon.2017.08.007 · Cited by 51
Aranda, L. (2015) · Social Science & Medicine · doi:10.1016/j.socscimed.2015.03.056 · Cited by 50
Doyle, W. R. (2010) · Economics of Education Review · doi:10.1016/j.econedurev.2010.08.004 · Cited by 51
Use and Interpretation of Propensity Scores in Aging Research: A Guide for Clinical Researchers
Kim, D. H., Pieper, C. F., Ahmed, A., & Colon-Emeric, C. S. (2016) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14253 · Cited by 54
“Finally, PS matching and weighting separates the design (creating a balanced cohort) from the analysis (estimating the treatment effect), which allows more-transparent analysis and limits data exploration.(46,47)”
MethodAdelson, J. L., McCoach, D. B., Rogers, H. J., Adelson, J. A., & Sauer, T. M. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01413 · Cited by 51
“Rubin (2001) presents three criteria for assessing the adequacy of the regression adjustment: (a) the difference in the means of the propensity scores in the groups being compared must be small, (b) the ratio of the variances of the propensity score in the treatment and comparison groups must be…”
BackgroundCeballos‐Concha, A., Dresdner, J., & Quiroga, M. (2018) · Food Policy · doi:10.1016/j.foodpol.2018.01.009 · Cited by 51
Firm Expectations and Economic Activity
Enders, Z., Hünnekes, F., & Müller, G. (2022) · Journal of the European Economic Association · doi:10.1093/jeea/jvac030 · Cited by 50
Elsayed, M., Morris, A., Li, X., Browne, L., & Stack, A. G. (2019) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfz278 · Cited by 54
Chen, Y., & Yang, X. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136855 · Cited by 56
“1, which meant that the treatment group and control group were well balanced [68].”
BackgroundTheory and practice of propensity score analysis
Hashimoto, Y., & Yasunaga, H. (2022) · Annals of Clinical Epidemiology · doi:10.37737/ace.22013 · Cited by 51
“First, PS methods work against p-hacking, as PS models are decided before looking at outcome data [31].”
MethodImpact of Telehealth and Process Virtualization on Healthcare Utilization
Ayabakan, S., Bardhan, I. R., & Zheng, Z. (2023) · Information Systems Research · doi:10.1287/isre.2023.1220 · Cited by 56
Ekundayo, O. J., Muchimba, M., Aban, I., Ritchie, C. S., Campbell, R. C., & Ahmed, A. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2008.08.035 · Cited by 49
Bründl, S., Matt, C., Heß, T., & Engert, S. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2062468 · Cited by 52
“1, variance ratios were close to 1, and eCDF statistics were close to 0 (Rubin, 2001; Stuart, 2010).”
BackgroundResultInfluentialChildhood sexual trauma and subsequent parenting beliefs and behaviors
Zvara, B. J., Mills-Koonce, W. R., Appleyard Carmody, K., & Cox, M. (2015) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2015.01.012 · Cited by 50
“Standardized biases of less than 0.25 imply the groups are well matched (Rubin, 2001).”
BackgroundInfluentialEffects of CenteringPregnancy Group Prenatal Care on Breastfeeding Outcomes
Tanner‐Smith, E. E., Steinka‐Fry, K. T., & Lipsey, M. W. (2013) · Journal of Midwifery & Women s Health · doi:10.1111/jmwh.12008 · Cited by 48
Ha, Y., & Park, H.-J. (2016) · Asia Pacific Education Review · doi:10.1007/s12564-016-9451-8 · Cited by 48
Financial Literacy and Gender Difference in Loan Defaults
Chen, J., Jiang, J., & Liu, Y. (2017) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2018.06.004 · Cited by 49
“We find that female investors have lower average income and lower 7 Rubin (2001) suggests that the samples are sufficiently balanced if Rubin’s B is less than 0.5 or if Rubin’s R is between 0.5 and 2.”
InfluentialThe limitations of observation studies for decision making regarding drugs efficacy and safety
Gueyffier, F., & Cucherat, M. (2018) · Therapies · doi:10.1016/j.therap.2018.11.001 · Cited by 50
“As mentioned by Rubin [25], in regression modeling there is always the temptation to work toward the desired or anticipated result.”
Kot, F. C., & Jones, J. L. (2015) · College & Research Libraries · doi:10.5860/crl.76.5.566 · Cited by 47
Ribas, L. G. D. S., Pressey, R. L., & Bini, L. M. (2021) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12697 · Cited by 49
“In these and other circumstances, it can be useful to select the covariates based on evidence taken from previous studies (Rubin, 2001), resulting in post-matching estimates with lower variance (Brookhart et al., 2006).”
BackgroundInfluentialPrevalence, Total and Excess Costs of Diabetes and Related Complications in Ontario, Canada
Goeree, R. A., Lim, M. E., Hopkins, R., Blackhouse, G., Tarride, J., Xie, F., & O’Reilly, D. J. (2009) · Canadian Journal of Diabetes · doi:10.1016/s1499-2671(09)31007-2 · Cited by 47
Stone, C. A. (2020) · doi:10.7275/qkqa-9k50 · Cited by 51
“Thus, “…if treatment and control groups have the same distribution of propensity scores, they have the same distribution for all observed covariates, just like in a randomized experiment” (Rubin, 2001; p. 171).”
Returns to rural electrification: Evidence from Bhutan
Litzow, E. L., Pattanayak, S., & Thinley, T. (2019) · World Development · doi:10.1016/j.worlddev.2019.04.002 · Cited by 44
Shariff, N., Desai, R. V., Patel, K., Ahmed, M. I., Fonarow, G. C., Rich, M. W., Aban, I., Banach, M., Love, T. E., White, M., et al. (2013) · The American Journal of Medicine · doi:10.1016/j.amjmed.2013.04.021 · Cited by 46
“Considering that the between-group balance achieved by randomization may be lost, at least in part, in the subgroup of patients 70-80 years of age, we used propensity score approach to assemble a cohort in which the rate-control and the rhythm-control groups would be balanced on key measured baseline confounders.(10,1…”
Lutz, W., Schiefele, A.-K., Wucherpfennig, F., Rubel, J. A., & Stulz, N. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.08.072 · Cited by 54
Bommaraju, R., & Hohenberg, S. (2018) · Journal of Marketing · doi:10.1509/jm.17.0002 · Cited by 51
“As shown in the right-most column of Table 3, the standardized mean differences on preprogram performance are much lower than the recommended value of .25 (Rubin 2001).”
BackgroundMatschinger, H., Heider, D., & König, H. (2020) · Das Gesundheitswesen · doi:10.1055/a-1009-6634 · Cited by 45
“As an example we will use the linear propensity score Dij = |logit(ei) − logit(ej)| as distance measure which is seen as very effective for reducing bias induced by confounders [24].”
MethodDo CRM Systems Cause One-to-One Marketing Effectiveness?
Mithas, S., Almirall, D., & Krishnan, M. S. (2006) · Statistical Science · doi:10.1214/088342306000000213 · Cited by 46
“Following Rubin (2001), we dropped from our analysis six non-CRM firms that fell outside the common support region (propensity scores for these firms are either less than 0.22 or more than 0.89).”
MethodInfluentialServing Persons With Severe Mental Illness in Primary Care–Based Medical Homes
Domino, M. E., Wells, R., & Morrissey, J. P. (2015) · Psychiatric Services · doi:10.1176/appi.ps.201300546 · Cited by 45
Frühübergang in ein grundständiges Gymnasium – Übergang in ein privilegiertes Entwicklungsmilieu?
Baumert, J., Becker, M., Neumann, M., & Nikolova, R. (2009) · Zeitschrift für Erziehungswissenschaft · doi:10.1007/s11618-009-0072-4 · Cited by 44
“5.5 Datenanalyse Analysestrategie: Im ersten Schritt wird die Klientel der grundständigen Gymnasialangebote im Vergleich zur Schülerschaft der anderen Sekundarschulformen multivariat beschrieben.”
BackgroundInfluentialBecker, M., Neumann, M., Tetzner, J., Böse, S., Knoppick, H., Maaz, K., Baumert, J., & Lehmann, R. (2014) · Journal of Educational Psychology · doi:10.1037/a0035425 · Cited by 50
Using Non-experimental Data to Estimate Treatment Effects
Stuart, E. A., Marcus, S. M., Horvitz-Lennon, M. V., Gibbons, R. D., Normand, S. T., & Brown, C. H. (2009) · Psychiatric Annals · doi:10.3928/00485713-20090625-07 · Cited by 44
Malone, S. M., Luciana, M. M., Wilson, S., Sparks, J. C., Hunt, R. H., Thomas, K. M., & Iacono, W. G. (2014) · Behavior Genetics · doi:10.1007/s10519-014-9651-0 · Cited by 47
“25) proposed as an indication that adjustment might not be trustworthy (Rubin 2001).”
InfluentialStuart, E. A., DuGoff, E. H., Abrams, M. T., Salkever, D. S., & Steinwachs, D. M. (2013) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1038 · Cited by 48
Peel, M. J. (2014) · Accounting and Business Research · doi:10.1080/00014788.2014.926249 · Cited by 45
“This is consistent with the simulation study of Guo and Fraser (2010) who report (p. 295) that PSM produced a more accurate estimate of the treatment effect than OLS regression in the presence of an unobserved confounding variable.”
InfluentialTanner‐Smith, E. E., Steinka‐Fry, K. T., & Lipsey, M. W. (2013) · Maternal and Child Health Journal · doi:10.1007/s10995-013-1304-z · Cited by 45
“The quality of the matching on the individual variables incorporated in the propensity scores was assessed by examining pre- and postmatching means, standardized mean differences, and variance ratios [21, 22] and showed that acceptable covariate balance was achieved at all sites (see [16] for standardized mean differe…”
Bhatia, V., Bajaj, N. S., Sanam, K., Hashim, T., Morgan, C. J., Prabhu, S. D., Fonarow, G. C., DEEDWANIA, P. C., Butler, J., Carson, P. E., et al. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.11.036 · Cited by 45
Patel, K., Fonarow, G. C., Ahmed, M. B., Morgan, C. J., Kilgore, M. L., Love, T. E., DEEDWANIA, P. C., Aronow, W. S., Anker, S. D., & Ahmed, A. (2014) · Circulation Heart Failure · doi:10.1161/circheartfailure.114.001301 · Cited by 45
“Aldosterone antagonists 580 (9) 48 (6) 0.”
(2013) · MDRC · Cited by 45
Sharma, V., WILSON, W. M., Smith, W. H. T., McEntegart, M. B., Oldroyd, K. G., Sidik, N. P., Bagnall, A. J., Egred, M., Irving, J., Strange, J., et al. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.11.004 · Cited by 45
Using propensity scores for causal inference in ecology: Options, considerations, and a case study
Ramsey, D. S. L., Forsyth, D. M., Wright, E. F., McKay, M., & Westbrooke, I. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13111 · Cited by 45
“In this situation, inference about treatment exposure will necessarily rely on strong model assumptions (Rubin, 2001).”
BackgroundInfluentialDesai, R. V., Banach, M., Ahmed, M. I., Mujib, M. U., Aban, I., Love, T. E., White, M., Fonarow, G. C., DEEDWANIA, P. C., Aronow, W. S., et al. (2010) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2010.02.032 · Cited by 45
Terrorist Attacks, Stereotyping, and Attitudes Toward Immigrants: The Case of the Manchester Bombing
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Patel, K., Fonarow, G. C., Kitzman, D. W., Aban, I., Love, T. E., Allman, R. M., Gheorghiade, M., & Ahmed, A. (2013) · JACC Heart Failure · doi:10.1016/j.jchf.2012.08.001 · Cited by 44
Financial Aid and College Persistence: Do Student Loans Help or Hurt?
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“With the exception of two covariates (dark lines), the standardized difference for all covariates dropped after matching (right), leaving all covariates well within +/- 0.25, a threshold range recommended for reliable regression adjustment (Stuart, 2010; Rubin, 2001).”
BackgroundInfluentialFancourt, D. E., & Steptoe, A. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00778 · Cited by 53
“(Rubin, 2001; Morgan, 2018).”
The impact of protected areas on the rural households’ incomes in Ethiopia
Estifanos, T. K., Polyakov, M., Pandit, R., Hailu, A., & Burton, M. P. (2019) · Land Use Policy · doi:10.1016/j.landusepol.2019.104349 · Cited by 46
Uncovering Waste in U.S. Healthcare
Doyle, J. J., Graves, J. A., & B, J. G. A. (2015) · Journal of Health Economics · doi:10.1016/j.jhealeco.2017.03.005 · Cited by 5
Başer, O. (2007) · Journal of Medical Economics · doi:10.3111/13696990701646577 · Cited by 44
A propensity matched study of the association of education and outcomes in chronic heart failure
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Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers
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Chen, W., Tran, T. N., Sadatsafavi, M., Murray, R., Wong, N. C. B., Ali, N., Ariti, C., Bulathsinhala, L., Gil, E. G., FitzGerald, J. M., et al. (2023) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2023.05.044 · Cited by 43
Comparative Effectiveness of Group and Individual Prenatal Care on Gestational Weight Gain
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“The quality of the matching on individual variables incorporated in the propensity scores was assessed by examining pre- and post-matching means, standardized mean differences, and variance ratios [21–24]; results indicated acceptable covariate balance was achieved (see Matern Child Health J (2014) 18:1711–1720 1713”
Zhang, H., Li, J., Chen, X., Wu, N., Xie, W., Tang, H., Li, C., Wu, L., Xiang, Y., Zhong, L., et al. (2017) · Heart Lung and Circulation · doi:10.1016/j.hlc.2017.04.007 · Cited by 43
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“The absolute standardized differences in means are all below the threshold of 0.25, suggesting a reasonable balance or matching between the two groups (Rubin 2001).”
BackgroundInfluentialGrade 1 Acute on Chronic Liver Failure Is a Predictor for Subsequent Grade 3 Failure
Mahmud, N., Sundaram, V., Kaplan, D. E., Taddei, T. H., & Goldberg, D. S. (2019) · Hepatology · doi:10.1002/hep.31012 · Cited by 43
“0 as metrics to determine adequate PSM matching, as recommended.(17) We also plotted standardized percent bias and kernel density plots between groups as visual confirmations of appropriate balance.”
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Maiti, A., Dinardo, C. D., Qiao, W., Kadia, T. M., Jabbour, E. J., Rausch, C. R., Daver, N. G., Short, N. J., Borthakur, G. M., Pemmaraju, N., et al. (2021) · Cancer · doi:10.1002/cncr.33814 · Cited by 43
“ELN 2017 risk group Favorable 10 (15) 11 (8) .”
Codagnone, C., Bogliacino, F., Gómez, C. E., Folkvord, F., Liva, G., Charris, R., Montealegre, F., Lupiáñez‐Villanueva, F., & Veltri, G. (2021) · Social Indicators Research · doi:10.1007/s11205-021-02697-5 · Cited by 44
“In order to consider a matching as sufficiently balanced, it is recommended to have a Rubins’ B less than 25 and a Rubins’ R between 0.5 and 2 (Rubin, 2001,).”
BackgroundInfluentialDicpinigaitis, A. J., Sursal, T., Morse, C. A., Briskin, C., Dakay, K., Kurian, C., Kaur, G., Sahni, R., Bowers, C. A., Gandhi, C. D., et al. (2021) · Stroke · doi:10.1161/strokeaha.121.034303 · Cited by 42
Mergers and acquisitions in supply bases
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“We assess the matching quality using several criteria (Rosenbaum & Rubin, 1983, 1985; Rubin, 2001).”
BackgroundMethodInfluentialThe long-term effects of loan guarantees on SME performance
Bertoni, F., Colombo, M. G., & Quas, A. (2023) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2023.102408 · Cited by 42
Tilahun, M., Maertens, M., Deckers, J., Muys, B., & Mathijs, E. (2015) · Forest Policy and Economics · doi:10.1016/j.forpol.2015.08.009 · Cited by 43
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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