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.
“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
Directed acyclic graphs were used 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.
“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
Variable selection for matching can be based on previous research.
“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
Principal Stratification in Causal Inference
Frangakis, C., & Rubin, D. B. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00021.x · Cited by 1,543
Self-citationMatching using estimated propensity scores: relating theory to practice.
Rubin, D., & Thomas, N. (1996) · Biometrics · doi:10.1017/cbo9780511810725.024 · Cited by 1,445
Assignment to Treatment Group on the Basis of a Covariate
Rubin, D. B. (1977) · Journal of Educational Statistics · doi:10.3102/10769986002001001 · Cited by 1,362
Self-citationRosenbaum, P., & Rubin, D. B. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01242.x · Cited by 1,128
Self-citationMatching Using Estimated Propensity Scores: Relating Theory to Practice
Rubin, D. B., & Thomas, N. (1996) · Biometrics · doi:10.2307/2533160 · Cited by 1,097
Self-citationHeckman, J. J., & Hotz, V. J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290059 · Cited by 969
Controlling Bias in Observational Studies: A Review
Cochran, W. G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.005 · Cited by 955
Heckman, J. J. (1989) · National Bureau of Economic Research · doi:10.3386/w2861 · Cited by 892
Rubin, D. B. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10482513 · Cited by 861
Self-citationCausal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs
Dehejia, R., & Wahba, S. (1998) · National Bureau of Economic Research · doi:10.3386/w6586 · Cited by 821
Matching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737
Self-citationOptimal Matching for Observational Studies
Rosenbaum, P. R. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478868 · Cited by 722
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Gu, X. S., & Rosenbaum, P. R. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474623 · Cited by 702 · cited 1×
“…score, and possibly other covariates, and the discarding of all unmatched units (see Rosenbaum and Rubin (1983a, 1985); Rosenbaum (1989, 1991); Gu and Rosenbaum (1993); Rubin and Thomas (1992a,b, 1996, 2000); also see Cochran and Rubin (1973); and Rubin (1976a,b, 1979, 1980)) on discriminant…”
BackgroundMatching to Remove Bias in Observational Studies
Rubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.007 · Cited by 600
Self-citationThe Planning of Observational Studies of Human Populations
Cochran, W. G., & Chambers, S. P. (1965) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344179 · Cited by 569
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Rubin, D. B., & Thomas, N. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474233 · Cited by 568
Self-citationEvaluating the Econometric Evaluations of Training Programs with Experimental DataPreprint
Lalonde, R. (1984) · RePEc: Research Papers in Economics · Cited by 557
Bias Reduction Using Mahalanobis-Metric Matching
Rubin, D. B. (1980) · Biometrics · doi:10.2307/2529981 · Cited by 539
Self-citationAnalysis of Covariance: Its Nature and Uses
Cochran, W. G. (1957) · Biometrics · doi:10.2307/2527916 · Cited by 511
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Thomas, N. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.025 · Cited by 508
The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies
Rubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.008 · Cited by 441
Self-citationSTRATIFICATION BY A MULTIVARIATE CONFOUNDER SCORE
Miettinen, O. S. (1976) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112339 · Cited by 351
A Characterization of Optimal Designs for Observational Studies
Rosenbaum, P. R. (1991) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1991.tb01848.x · Cited by 331
Statistical Methods for Comparative Studies
Anderson, S., Auquier, A., Hauck, W. W., Oakes, D., Vandaele, W., & Weisberg, H. I. (1980) · Wiley series in probability and statistics · doi:10.1002/9780470316498 · Cited by 312
In Utero Exposure to Phenobarbital and Intelligence Deficits in Adult Men
Reinisch, J. M., Sanders, S. A., Mortensen, E. L., & Rubin, D. B. (1995) · JAMA · doi:10.1001/jama.1995.03530190032031 · Cited by 310
Self-citationEstimating and Using Propensity Scores with Partially Missing Data
D'Agostino, R. B., & Rubin, D. B. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474263 · Cited by 303
Self-citationStatistical Methods for Comparative Studies.
Greenfield, T., Anderson, S., Auquier, A., Hauck, W. W., Oakes, D., Vandaele, W., & Weisberg, H. I. (1981) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988054 · Cited by 251
In utero exposure to phenobarbital and intelligence deficits in adult men
Reinisch, J. M. (1995) · JAMA · doi:10.1001/jama.274.19.1518 · Cited by 239
Rubin, D. B., & Thomas, N. (1992) · Biometrika · doi:10.1093/biomet/79.4.797 · Cited by 189
Self-citationMultivariate Matching Methods That are Equal Percent Bias Reducing, I: Some Examples
Rubin, D. B. (1976) · Biometrics · doi:10.2307/2529342 · Cited by 161
Self-citationAffinely Invariant Matching Methods with Ellipsoidal Distributions
Rubin, D. B., & Thomas, N. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348671 · Cited by 121
Self-citationRubin, D. B. (1976) · Biometrics · doi:10.2307/2529343 · Cited by 118
Self-citationRubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.013 · Cited by 112
Self-citationProjecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics
Czajka, J. L., Hirabayashi, S. M., Little, R. J. A., & Rubin, D. B. (1992) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1992.10509892 · Cited by 72
Self-citationA Method of Matching Groups for Experiment with No Loss of Population
Peters, C. C. (1941) · The Journal of Educational Research · doi:10.1080/00220671.1941.10881036 · Cited by 68
Matching when Covariables are Normally Distributed
Carpenter, R. G. (1977) · Biometrika · doi:10.1093/biomet/64.2.299 · Cited by 51
Estimating the causal effects of smoking
Rubin, D. B. (2001) · Statistics in Medicine · doi:10.1002/sim.677 · Cited by 34
Self-citationRubin, D. B. (2000) · Springer eBooks · doi:10.1007/978-1-4612-1216-4_16 · Cited by 13
Self-citationStatistical Testimony on Damages in Minnesota v. Tobacco Industry
Zeger, S. L., Wyant, T., Miller, L. S., & Samet, J. (2000) · Springer eBooks · doi:10.1007/978-1-4612-1216-4_15 · Cited by 7
Flagged first, then by citations
2 of the 51 references couldn’t be matched to a known paper.
Research works printed but not linked: 10
- AHCPR, “National medical expenditure survey, calendar year 1987,” Center for General Health Services Research, Agency for Health Care Policy and Research, Public Health Service: Rockville,MD, 1992.
- D. J. Benjamin, “Does 401(k) eligibility increase net national savings?: reducing bias in the eligibility effect estimate,” A. B. Honors Thesis in Economics, Harvard University, Cambridge, MA, 1999.
- GAO (U.S. General Accounting Office), “Breast conservation versus mastectomy: patient survival in day-to-day medical practice and randomized studies,” Report #GAO-PEMD-95-9, U.S. General Accounting Office: Washington, D.C., 1995.
- G. W. Harrison, ‘Expert Report, April 27, 1998: “Health care expenditures attributable to smoking in Oklahoma,”’ The State of Oklahoma, ex rel., et al., Plaintiffs, vs. Reynolds Tobacco Co., et al., Defendants, Case No. CJ-96-1499-L, District Court of Cleveland County, Oklahoma, 1998.
- J. Hill, D. B. Rubin and N. Thomas, “The design of the New York school choice scholarship program evaluation,” in Research Designs: Inspired by the Work of Donald Campbell, (L. Bickman, ed.), Sage Publications, Thousand Oaks, CA, 155-180, 1999.
- L. Roseman, “Reducing bias in the estimate of the difference in survival in observational studies using subclassification on the propensity score,” Ph.D. Thesis, Department of Statistics, Harvard University, Cambridge, MA, 1998.
- P. R. Rosenbaum and D. B. Rubin, “Constructing a control group using multivariate matched sampling incorporating the propensity score,” The American Statistician, 39, pp. 33-38, 1985. · DOI
- D. B. Rubin, “The use of matched sampling and regression adjustment in observational studies,” Ph.D. Thesis, Department of Statistics, Harvard University: Cambridge, MA, 1970.
- D. B. Rubin, “William, G. Cochran's contributions to the design, analysis, and evaluation of observational studies,” in W. G. Cochran's Impact on Statistics ( Rao and Sedransk, eds.), John Wiley, New York, pp. 37-69, 1984.
- D. B. Rubin, “Statistical assumptions in the estimation of the causal effects of smoking due to the conduct of the tobacco industry,” in Social Science Methodology in the New Millennium. Proceedings of the Fifth International Conference on Logic and Methodology (J. Blasius, J. Hox, E. de Leeuw and P. Schmidt, eds.), October 6, 2000, Cologne, Germany, 1-22, 2000b.
Cited by
Sorted by citations
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
“Rubin (2001) proposed a set of criteria based on comparing the distribution of the propensity score between treated and untreated subjects in a sample to determine whether regression adjustment may inadequately eliminate bias when comparing outcomes between treatment groups.”
BackgroundMethodInfluentialAustin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
“Rubin argues that an advantage to the use of propensity-score methods is that it allows observational studies to be designed similar to randomized experiments: the design of the study is separated from the analysis of the effect of exposure on the outcome [10].”
Abadie, A., Diamond, A., & Hainmueller, J. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.ap08746 · Cited by 5,513
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495
“Rubin argues that an advantage to the use of propensity score methods is that they allow observational studies to be designed similar to randomized experiments: the design of the study is separated from the analysis of the effect of exposure on the outcome [5].”
BackgroundMatching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,268
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563
Sekhon, J. S. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i07 · Cited by 1,521
“Mahalanobis distance and propensity score matching can be combined in various ways (Rubin 2001; Rosenbaum and Rubin 1985).”
A tutorial on propensity score estimation for multiple treatments using generalized boosted models
McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013) · Statistics in Medicine · doi:10.1002/sim.5753 · Cited by 1,450
“This feature of propensity score adjustments is valuable because it eliminates the potential for the choice of model specification for pretreatment variables to be influenced by its impact on the estimated treatment effect [8].”
BackgroundAustin, P. C., Grootendorst, P. V., & Anderson, G. M. (2006) · Statistics in Medicine · doi:10.1002/sim.2580 · Cited by 1,138
“Rubin suggests that one of the advantages of designing a study using propensity score methods is that one can design the study prior to any outcome being in mind [20].”
BackgroundNock, M. K., Hwang, I., Sampson, N. A., & Kessler, R. C. (2010) · Molecular Psychiatry · doi:10.1038/mp.2009.29 · Cited by 1,125
Diamond, A., & Sekhon, J. S. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00318 · Cited by 1,100
8. Misunderstandings Between Experimentalists and Observationalists About Causal Inference
Imai, K., King, G., & Stuart, E. A. (2008) · Yale University Press eBooks · doi:10.1111/j.1467-985x.2007.00527.x · Cited by 925
“Other options include higher order moments than the mean, non-parametric density plots and propensity score summary statistics (e.g. Austin and Mamdani (2006) and Rubin (2001)).”
BackgroundImproving propensity score weighting using machine learning
Lee, B. K., Lessler, J., & Stuart, E. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3782 · Cited by 920
“Although some researchers have recommended checking balance on interactions [36, 37], unfortunately it is rarely done in practice.”
BackgroundMethods for Constructing and Assessing Propensity Scores
Garrido, M. M., Kelley, A. S., Paris, J., Roza, K. A., Meier, D. E., Morrison, R. S., & Aldridge, M. D. (2014) · Health Services Research · doi:10.1111/1475-6773.12182 · Cited by 925
Rubin, D. B. (2007) · Statistics in Medicine · doi:10.1002/sim.2739 · Cited by 1,047
“Tables II and III (modified from [24]) summarize a sequence of analyses, all conducted using only the variables in Table I, that is, with no medical expenditure data or cancer rate or other outcome data in sight.”
BackgroundMethodInfluentialSelf-citationFor objective causal inference, design trumps analysis
Rubin, D. B. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas187 · Cited by 831
Self-citationAbadie, A., Diamond, A., & Hainmueller, J. (2007) · National Bureau of Economic Research · doi:10.3386/t0335 · Cited by 830
Cost Savings Associated With US Hospital Palliative Care Consultation Programs
Morrison, R. S. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.16.1783 · Cited by 810
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802
“Our study uses the standpoint of clearly separating “design” and “analysis” phases for causal inference, as recommended by Rubin and colleagues (Rubin, 2001; Stuart & Rubin, 2007; Rubin, 2008).”
BackgroundMethodResultInfluentialStuart, E. A., Lee, B. K., & Leacy, F. P. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.01.013 · Cited by 835
“25 represent reasonable cutoffs for acceptable standardized biases; larger standardized biases indicate that groups are too different from one another for reliable comparison [15].”
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · Thammasat University Digital Collections · Cited by 838
Self-citationMcMullin, J. L., & Schonberger, B. (2020) · Review of Accounting Studies · doi:10.1007/s11142-019-09525-9 · Cited by 761
Matched Sampling for Causal Effects: Contributor Acknowledgments
Rubin, D. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725 · Cited by 287
Self-citationCook, T. D., Shadish, W. R., & Wong, V. C. (2008) · Journal of Policy Analysis and Management · doi:10.1002/pam.20375 · Cited by 604
“…study analysis required computing adjusted observational study effect sizes via OLS and propensity scores that met the balance criteria in Rubin (2001), and were carried by an author blind to how the propensity scores he computed affected the bias reduction achieved in a particular…”
BackgroundMethodAustin, P. C., & Mamdani, M. (2005) · Statistics in Medicine · doi:10.1002/sim.2328 · Cited by 560
Competing with complementors: An empirical look at Amazon.com
Zhu, F., & Liu, Q. (2018) · Strategic Management Journal · doi:10.1002/smj.2932 · Cited by 546
The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
Stuart, E. A., Cole, S. R., Bradshaw, C. P., & Leaf, P. J. (2011) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00673.x · Cited by 524
Average causal effects from nonrandomized studies: A practical guide and simulated example.
Schafer, J. L., & Kang, J. (2008) · Psychological Methods · doi:10.1037/a0014268 · Cited by 550
Weight Trimming and Propensity Score Weighting
Lee, B. K., Lessler, J., & Stuart, E. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018174 · Cited by 529
“, the weighted or matched samples), ensuring that the groups are comparable with respect to the observed covariates (see Stuart, 2010 [9], or Rubin, 2001 [10], for further discussion of propensity score diagnostics).”
BackgroundShadish, W. R., Clark, M. H., & Steiner, P. M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000733 · Cited by 479
“score matches to a smaller group of treatment group participant scores (Rosenbaum and Rubin, 1985; Rubin, 2001 ).”
BackgroundMethodInfluentialThe importance of covariate selection in controlling for selection bias in observational studies.
Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H. (2010) · Psychological Methods · doi:10.1037/a0018719 · Cited by 490
“After PS-adjustment, standardized mean differences d should be close to zero, and variance ratios v should be close to one (Rubin, 2001).”
BackgroundPalliative Care Consultation Teams Cut Hospital Costs For Medicaid Beneficiaries
Morrison, R. S., Dietrich, J., Ladwig, S., Quill, T. E., Sacco, J., Tangeman, J. C., & Meier, D. E. (2011) · Health Affairs · doi:10.1377/hlthaff.2010.0929 · Cited by 469
Ahmed, A., Husain, A., Love, T. E., Gambassi, G., Dell’Italia, L. J., Francis, G. S., Gheorghiade, M., Allman, R. M., Meleth, S., & Bourge, R. C. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehi890 · Cited by 469
Austin, P. C. (2009) · Medical Decision Making · doi:10.1177/0272989x09341755 · Cited by 455
“047 Respiratory rate, breaths per minute 24 (20-30) 24 (20-30) 0.”
β-Blocker Use and Clinical Outcomes in Stable Outpatients With and Without Coronary Artery Disease
Bangalore, S., Steg, G., Deedwania, P., Crowley, K., Eagle, K. A., Goto, S., Ohman, E. M., Cannon, C. P., Smith, S. C., Zeymer, U., et al. (2012) · JAMA · doi:10.1001/jama.2012.12559 · Cited by 436
Does Auditor Industry Specialization Improve Audit Quality?
Minutti‐Meza, M. (2013) · Journal of Accounting Research · doi:10.1111/1475-679x.12017 · Cited by 417
Ahmed, A., Rich, M. W., Love, T. E., Lloyd‐Jones, D. M., Aban, I., Colucci, W. S., Adams, K. F., & Gheorghiade, M. (2005) · European Heart Journal · doi:10.1093/eurheartj/ehi687 · Cited by 408
Causal Inference in Sociological Research
Gangl, M. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102702 · Cited by 422
Using the Propensity Score Method to Estimate Causal Effects
Li, M. (2012) · Organizational Research Methods · doi:10.1177/1094428112447816 · Cited by 382
“Rubin (2001) provided additional discussion on covariate adjustment.”
BackgroundAmant, F. C., von Minckwitz, G., Han, S., Bontenbal, M., Ring, A. E., Giermek, J., Wildiers, H. P. M. W., Fehm, T. N., Linn, S. C., Schlehe, B., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.6335 · Cited by 365
“Given that the average age at diagnosis was clearly different for pregnant and nonpregnant patients, simply adjusting for age by using age as a covariate may not have been sufficient.(30) Instead, we adjusted for age using inverse probability weighting to statistically correct for the strong imbalance in this variable.”
MethodBowe, B. C., Xie, Y., Xu, E., & Al-Aly, Z. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021060734 · Cited by 352
“15 was taken as evidence of balance between the COVID-19 and VHA users group.(21,22) We estimated the excess burden per 1000 persons of the outcomes associated with COVID-19 at 6 months after T0, where excess burden was estimated by computing the difference between the average estimated survival probability from the w…”
MethodBest Practices in Quasi–Experimental Designs: Matching Methods for Causal Inference
Stuart, E. A., & Rubin, D. B. (2008) · Sage eBooks · doi:10.4135/9781412995627.d14 · Cited by 356
Self-citationZiff, O. J., Lane, D. A., Samra, M., Griffith, M., Kirchhof, P., Lip, G. Y. H., Steeds, R. P., Townend, J., & Kotecha, D. (2015) · BMJ · doi:10.1136/bmj.h4451 · Cited by 341
Terrorist Events and Attitudes toward Immigrants: A Natural Experiment
Legewie, J. (2013) · American Journal of Sociology · doi:10.1086/669605 · Cited by 353
Risks of mental health outcomes in people with covid-19: cohort study
Xie, Y., Xu, E., & Al-Aly, Z. (2022) · BMJ · doi:10.1136/bmj-2021-068993 · Cited by 349
Opiates for the Matches: Matching Methods for Causal Inference
Sekhon, J. S. (2009) · Annual Review of Political Science · doi:10.1146/annurev.polisci.11.060606.135444 · Cited by 328
“…2003, Rubin 1997), economics (Abadie & Imbens 2006a; Dehejia & Wahba 2002, 1999; Galiani et al. 2005), political science (Bowers & Hansen 2005, Imai 2005, Sekhon 2004), sociology (Diprete & Engelhardt 2004, Morgan & Harding 2006, Smith 1997, Winship & Morgan 1999), and even law (Rubin 2001).”
BackgroundStuart, E. A., & Green, K. M. (2008) · Developmental Psychology · doi:10.1037/0012-1649.44.2.395 · Cited by 328
“However, weighting adjustments can be thought of as the limit of subclassification, as the number of subclasses and observations go to infinity, and so we do not discuss it as a separate method (Rubin, 2001).”
BackgroundMethodInfluentialAssessing the generalizability of randomized trial results to target populations
Stuart, E. A., Bradshaw, C. P., & Leaf, P. J. (2015) · Prevention Science · doi:10.1007/s11121-014-0513-z · Cited by 296
“2 standard deviations as a difference that indicates unacceptable differences between the groups (in this case, the sample and the population), which would result in extrapolation and model dependence (Rubin 2001; Stuart 2010).”
InfluentialSilber, J. H., Rosenbaum, P. R., Clark, A. S., Giantonio, B. J., Ross, R. N., Teng, Y., Wang, M., Niknam, B. A., Ludwig, J. M., Wang, W., et al. (2013) · JAMA · doi:10.1001/jama.2013.8272 · Cited by 308
Propensity score matching with R: conventional methods and new features
Zhao, Q., Luo, J., Su, Y., Zhang, Y., Tu, G.-W., & Luo, Z. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-3998 · Cited by 320
On principles for modeling propensity scores in medical research
Rubin, D. B. (2004) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.968 · Cited by 288
“This is a theme that can be traced to classical work(1) in observational studies, and a theme I have recently emphasized in the context of the tobacco litigation.(2) When we design a randomized experiment, we cannot try one randomization and see the answer, then try another randomization and see the answer, and contin…”
BackgroundSelf-citationGordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019) · Marketing Science · doi:10.1287/mksc.2018.1135 · Cited by 287
“This pair of conditions is commonly known as the Stable Unit Treatment Value Assumption (SUTVA), a term coined in Rubin (1978).”
Everolimus-Eluting Stents or Bypass Surgery for Multivessel Coronary Disease
Bangalore, S., Guo, Y., Samadashvili, Z., Blecker, S., Xu, J., & Hannan, E. L. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1412168 · Cited by 286
Tinetti, M. E., Naik, A. D., Dindo, L. N., Costello, D. M., Esterson, J., Geda, M. E., Rosen, J. D., Hernandez‐Bigos, K., Smith, C. D., Ouellet, G. M., et al. (2019) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.4235 · Cited by 284
Exporting, productivity and agglomeration
Greenaway, D., & Kneller, R. (2007) · European Economic Review · doi:10.1016/j.euroecorev.2007.07.001 · Cited by 276
Semiparametric Regression for the Social Sciences
Keele, L. J. (2007) · Wiley eBooks · doi:10.1002/9780470998137 · Cited by 260
Protected areas have a mixed impact on waterbirds, but management helps
Wauchope, H. S., Jones, J. P. G., Geldmann, J., Simmons, B. I., Amano, T., Blanco, D. E., Fuller, R. A., Johnston, A., Langendoen, T., Mundkur, T., et al. (2022) · Nature · doi:10.1038/s41586-022-04617-0 · Cited by 265
Ahmed, A., Rich, M. W., Sanders, P. W., Perry, G. J., Bakris, G. L., Zile, M. R., Love, T. E., Aban, I., & Shlipak, M. G. (2006) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2006.08.042 · Cited by 241
Austin, P. C. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1673 · Cited by 240
Alix‐Garcia, J., Sims, K. R. E., & Yáñez-Pagans, P. (2015) · American Economic Journal Economic Policy · doi:10.1257/pol.20130139 · Cited by 247
“Post-matching, none of the normalized differences are greater than .15 standard deviations, which is below the suggested rule of thumb of .25 standard deviations (Rubin 2001, Imbens and Wooldridge 2009).”
BackgroundA Potential Outcomes View of Value-Added Assessment in Education
Rubin, D. B., Stuart, E. A., & Zanutto, E. L. (2004) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986029001103 · Cited by 233
“…we approach value-added assessment from a “potential outcomes” (Rubin Causal Model, RCM) point of view (Rubin 1974, 1978, 2003; Holland 1986; Little and Rubin 2001), with the goal of clarifying the estimation goals and understanding the limitations of data for the types of comparisons being sought.”
BackgroundInfluentialSelf-citationStatistical matching for conservation science
Schleicher, J., Eklund, J., Barnes, M. D., Geldmann, J., Oldekop, J. A., & Jones, J. P. G. (2019) · Conservation Biology · doi:10.1111/cobi.13448 · Cited by 244
“Standard tests and visualizations that explore match quality have been widely published in the statistical, economics, health, and political science literatures (e.g., Rubin 2001; Harris & Horst 2016).”
BackgroundCimpian, J. R., Lubienski, S. T., Ganley, C. M., & Copur‐Gencturk, Y. (2013) · Developmental Psychology · doi:10.1037/a0035073 · Cited by 251
Propensity score matching and variations on the balancing test
Lee, W.-S. (2011) · Empirical Economics · doi:10.1007/s00181-011-0481-0 · Cited by 223
A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
Thoemmes, F. J., & Ong, A. D. (2015) · Emerging Adulthood · doi:10.1177/2167696815621645 · Cited by 226
“Finally, as others have noted (Rubin, 2001), the ease with which regression models with different numbers of covariates can be estimated and evaluated may introduce biases through cherry-picking models that produce desired outcomes.”
BackgroundExtending inferences from a randomized trial to a new target population
Dahabreh, I. J., Robertson, S. E., Steingrimsson, J. A., Stuart, E. A., & Hernán, M. Á. (2020) · Statistics in Medicine · doi:10.1002/sim.8426 · Cited by 207
“Covariate balance assessments [57] comparing the postweighting covariate distribution in trial participants against the (unweighted) covariate distribution in non-participants can provide some guidance for the choice of functional form.”
BackgroundThe Renaissance of Field Experimentation in Evaluating Interventions
Shadish, W. R., & Cook, T. D. (2008) · Annual Review of Psychology · doi:10.1146/annurev.psych.60.110707.163544 · Cited by 208
Exporting and Productivity in the United Kingdom
Greenaway, D., & Greenaway, D. (2004) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grh021 · Cited by 211
Regional economic impacts of the shale gas and tight oil boom: A synthetic control analysis
Munasib, A., & Rickman, D. S. (2014) · Regional Science and Urban Economics · doi:10.1016/j.regsciurbeco.2014.10.006 · Cited by 202
Ali, M. S., Prieto-Alhambra, D., Lopes, L. C., Ramos, D., Bispo, N., Ichihara, M. Y., Pescarini, J. M., Williamson, E., Fiaccone, R. L., Barreto, M. L., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00973 · Cited by 200
“Similar to matching, residual imbalances after stratification can be accounted for using regression adjustment within each stratum (Rosenbaum and Rubin, 1984; Rubin, 2001).”
BackgroundMethodInfluentialRiosmena, F., Kuhn, R., & Jochem, W. C. (2017) · Demography · doi:10.1007/s13524-016-0542-2 · Cited by 210
“number of treatment or control cases would be dropped because of noncomparability (Baser 2007; Rubin 2001).”
Gheorghiade, M., Fonarow, G. C., van Veldhuisen, D. J., Cleland, J. G. F., Butler, J., Epstein, A. E., Patel, K., Aban, I. B., Aronow, W. S., Anker, S. D., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht120 · Cited by 192
When Good Balance Goes Bad: A Discussion of Common Pitfalls When Using Entropy Balancing
McMullin, J. L., & Schonberger, B. (2022) · Journal of Financial Reporting · doi:10.2308/jfr-2021-007 · Cited by 188
Gender Differences in Outcomes After Hospital Discharge From Coronary Artery Bypass Grafting
Guru, V., Fremes, S. E., Austin, P. C., Blackstone, E. H., & Tu, J. V. (2006) · Circulation · doi:10.1161/circulationaha.105.576652 · Cited by 189
Austin, P. C. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1674 · Cited by 188
Research subsidies, industry–university cooperation and innovation
Szücs, F. (2018) · Research Policy · doi:10.1016/j.respol.2018.04.009 · Cited by 200
Ahmed, A., Zannad, F. M., Love, T. E., Tallaj, J. A., Gheorghiade, M., Ekundayo, O. J., & Pitt, B. (2007) · European Heart Journal · doi:10.1093/eurheartj/ehm091 · Cited by 187
Innovation and SME finance: Evidence from developing countries
Wellalage, N. H., & Fernandez, V. P. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2019.06.009 · Cited by 184
West, S. G., Cham, H., & Thoemmes, F. J. (2015) · Wiley eBooks · doi:10.1002/9781118625392.wbecp505 · Cited by 185
Using balance statistics to determine the optimal number of controls in matching studies
Linden, A., & Samuels, S. J. (2013) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12072 · Cited by 180
“Matching is often favoured over other adjustment techniques, such as regression analysis, because it decouples the design from the analysis of treatment effects [14].”
BackgroundInfluentialGeneralized propensity score for estimating the average treatment effect of multiple treatments
Feng, P., Zhou, X., Zou, Q., Fan, M., & Li, X. (2011) · Statistics in Medicine · doi:10.1002/sim.4168 · Cited by 180
Donkor, E., Owusu‐Sekyere, E., Owusu, V., & Jordaan, H. (2016) · NJAS - Wageningen Journal of Life Sciences · doi:10.1016/j.njas.2016.10.002 · Cited by 177
Asymmetric Information and Imperfect Competition in Lending Markets
Crawford, G. S., Pavanini, N., & Schivardi, F. (2018) · American Economic Review · doi:10.1257/aer.20150487 · Cited by 214
Sampogna, G., Bakolis, I., Evans-Lacko, S. E., Robinson, E. J., Thornicroft, G., & Henderson, C. (2017) · European Psychiatry · doi:10.1016/j.eurpsy.2016.08.008 · Cited by 176
“A propensity score was calculated in order to adjust for the likelihood of participants’ awareness of the TTC campaign in each burst [43,44].”
MethodMcMurry, T. L., Hu, Y., Blackstone, E. H., & Kozower, B. D. (2015) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2015.03.057 · Cited by 178
“Alternatively, Rubin suggests a set of powerful, but less intuitive, diagnostics.(17)”
BackgroundHousehold vulnerability and child labor: the effect of shocks, credit rationing, and insurance
Guarcello, L., Mealli, F., & Rosati, F. C. (2009) · Journal of Population Economics · doi:10.1007/s00148-008-0233-4 · Cited by 182
The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods
Sekhon, J. S. (2009) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199286546.003.0011 · Cited by 175
Ahmed, A., Allman, R. M., Fonarow, G. C., Love, T. E., Zannad, F. M., Dell’Italia, L. J., White, M., & Gheorghiade, M. (2008) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2007.12.001 · Cited by 167
Schoeler, T., Petros, N., Forti, M. D., Klamerus, E., Foglia, E., Ajnakina, O., Gayer-Anderson, C. E., Colizzi, M., Quattrone, D., Behlke, I., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30188-2 · Cited by 162
Influence of Time to Change's social marketing interventions on stigma in England 2009-2011
Evans-Lacko, S., Malcolm, E., West, K., Rose, D., London, J., Rüsch, N., Little, K., Henderson, C., & Thornicroft, G. (2013) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.126672 · Cited by 151
Matching for Causal Inference Without Balance CheckingPreprint
Iacus, S. M., King, G., & Porro, G. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1152391 · Cited by 154
Industry Differences in the Effect of Export Market Entry: Learning by Exporting?
Greenaway, D., & Kneller, R. (2007) · Review of World Economics · doi:10.1007/s10290-007-0115-y · Cited by 149
Hypokalemia and Outcomes in Patients With Chronic Heart Failure and Chronic Kidney Disease
Bowling, C. B., Pitt, B., Ahmed, M. I., Aban, I., Sanders, P. W., Mujib, M. U., Campbell, R. C., Love, T. E., Aronow, W. S., Allman, R. M., et al. (2010) · Circulation Heart Failure · doi:10.1161/circheartfailure.109.899526 · Cited by 152
Bangalore, S., Guo, Y., Samadashvili, Z., Blecker, S., & Hannan, E. L. (2016) · Circulation · doi:10.1161/circulationaha.115.021168 · Cited by 152
“The propensity score is a conditional probability of having a particular exposure (EES versus CABG) given a set of baseline-measured covariates.(7,8) Propensity scores were calculated using a nonparsimonious multivariable logistic regression model(9) using EES use as the dependent variable and the baseline characteris…”
MethodEstimation of Causal Effects with Multiple Treatments: A Review and New Ideas
Lopez, M. J., & Gutman, R. (2017) · Statistical Science · doi:10.1214/17-sts612 · Cited by 157
“In order to provide advice to investigators and following Rubin (2001), we generated simulation configurations that are either known or can be estimated from the data.”
BackgroundMethodInfluentialBecker, M., Lüdtke, O., Trautwein, U., Köller, O., & Baumert, J. (2012) · Journal of Educational Psychology · doi:10.1037/a0027608 · Cited by 164
Steiner, P. M., Cook, T. D., & Shadish, W. R. (2011) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998610375835 · Cited by 152
“1 SD—and a variance ratio close to one (Rubin, 2001).”
BackgroundUsing Propensity Scores for Causal Inference: Pitfalls and Tips
Shiba, K., & Kawahara, T. (2021) · Journal of Epidemiology · doi:10.2188/jea.je20210145 · Cited by 146
Kobori, T., Onishi, Y., Yoshida, Y., Tahara, T., Kikuchi, T., Kubota, T., Iwamoto, M., Sawada, T., Kobayashi, R., Fujiwara, H., et al. (2023) · Journal of Diabetes Investigation · doi:10.1111/jdi.14005 · Cited by 144
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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