Matching Methods for Causal Inference: A Review and a Look Forward

Stuart, Elizabeth A.

doi:10.1214/09-sts313

Cited by 5,478Advanced Causal Inference TechniquesStatistical Methods and InferenceOptimal Experimental Design Methods

Matching Methods for Causal Inference: A Review and a Look Forward

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Stuart, Elizabeth A.·2010·Statistical Science

DOI 10.1214/09-sts313PMC PMC2943670

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Abstract

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

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Rosenbaum and Rubin demonstrated that treated and control subjects with the same propensity score have the same distribution relative to the observed covariates.
“Rosenbaum and Rubin demonstrated that treated and control subjects with the same propensity score have the same distribution relative to the observed covariates.32”
From The Impact of Library Resource Utilization on Undergraduate Students’ Academic Performance: A Propensity Score Matching Design (2015) · cites this paper as [32]
Given the advantages and disadvantages of matching techniques and the small number of available covariates, the choice of calliper matching in this study is valid.
“In view of the advantages and disadvantages of the various matching techniques and due to the small number of available possible covariates, we consider that the choice of calliper method for the matching purpose in the present study is valid [48].”
From Impact of m‐health application used by community health volunteers on improving utilisation of maternal, new‐born and child health care services in a rural area of Uttar Pradesh, India (2017) · cites this paper as [48]
Variables thought to be related to the outcome were preferred for matching to reduce variance from including unrelated variables.
“Preference was given to the variables believed to be related to the outcome of interest; thereby reducing the variance engendered due to the inclusion of variables which are unrelated to the outcome [48,58].”
From Impact of m‐health application used by community health volunteers on improving utilisation of maternal, new‐born and child health care services in a rural area of Uttar Pradesh, India (2017) · cites this paper as [48]

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Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,406 · see citing works
Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lachenbruch, P. A., & Cohen, J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290095 · Cited by 37,385 · see citing works
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 · see citing works
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works
Inference and missing data
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925 · see citing works
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436 · see citing works
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
Rosenbaum, P., & Rubin, D. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,854 · see citing works
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581 · see citing works
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997) · The Review of Economic Studies · doi:10.2307/2971733 · Cited by 5,486 · see citing works
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293 · see citing works
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Dehejia, R., & Wahba, S. (2002) · The Review of Economics and Statistics · doi:10.1162/003465302317331982 · Cited by 4,777 · see citing works
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479383 · Cited by 4,415 · see citing works
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,411 · see citing works
Self-citation
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 · see citing works
Matching As An Econometric Evaluation Estimator
Heckman, J. J., Ichimura, H., & Todd, P. (1998) · Review of Economic Studies · doi:10.1111/1467-937x.00044 · Cited by 3,807 · see citing works
The Central Role of the Propensity Score in Observational Studies for Causal Effects
Rosenbaum, P. R., & Rubin, D. B. (1983) · Biometrika · doi:10.2307/2335942 · Cited by 3,495 · see citing works
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 · see citing works
A Generalization of Sampling Without Replacement From a Finite Universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.2307/2280784 · Cited by 3,011 · see citing works
Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review
Imbens, G. W. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023651 · Cited by 2,994 · see citing works
Large sample properties of matching estimators for average treatment effects
Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908 · see citing works
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Hirano, K., Imbens, G. W., & Ridder, G. (2003) · Econometrica · doi:10.1111/1468-0262.00442 · Cited by 2,525 · see citing works
Observational Studies
Rosenbaum, P. R. (2002) · Springer series in statistics · doi:10.1007/978-1-4757-3692-2 · Cited by 2,414 · see citing works
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 · see citing works
Variable Selection for Propensity Score Models
Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj149 · Cited by 2,336 · see citing works
Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research
Austin, P. (2009) · Communications in statistics. Simulation and computation · doi:10.1080/03610910902859574 · Cited by 2,323 · see citing works
Characterizing Selection Bias Using Experimental Data
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998) · Econometrica · doi:10.2307/2999630 · Cited by 2,286 · see citing works
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
Rubin, D. B. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020363010465 · Cited by 2,228 · see citing works
Doubly robust estimation in missing data and causal inference models
Bang, H., & Robins, J. M. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00377.x · Cited by 1,998 · see citing works
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 1,747 · see citing works
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Lunceford, J. K., & Davidian, M. (2004) · Statistics in Medicine · doi:10.1002/sim.1903 · Cited by 1,676 · see citing works
Principal Stratification in Causal Inference
Frangakis, C., & Rubin, D. B. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00021.x · Cited by 1,496 · see citing works
Matching using estimated propensity scores: relating theory to practice.
Rubin, D., & Thomas, N. (1996) · Biometrics · doi:10.1017/cbo9780511810725.024 · Cited by 1,445 · see citing works
Bias-Corrected Matching Estimators for Average Treatment Effects
Abadie, A., & Imbens, G. W. (2011) · Journal of Business & Economic Statistics · doi:10.1198/jbes.2009.07333 · Cited by 1,416 · see citing works
Propensity score estimation with boosted regression for evaluating causal effects in observational studies
McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.403 · Cited by 1,317 · see citing works
Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome
Rosenbaum, P., & Rubin, D. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01242.x · Cited by 1,264 · see citing works
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
Kang, J. D. Y., & Schafer, J. L. (2007) · Statistical Science · doi:10.1214/07-sts227 · Cited by 1,259 · see citing works
Design of Observational Studies
Rosenbaum, P. R. (2010) · Springer Series in Statistics · doi:10.1007/978-1-4419-1213-8 · Cited by 1,150 · see citing works
Invited commentary: propensity scores.
Joffe, M., & Rosenbaum, P. (1999) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a010011 · Cited by 1,117 · see citing works
Matching Using Estimated Propensity Scores: Relating Theory to Practice
Rubin, D. B., & Thomas, N. (1996) · Biometrics · doi:10.2307/2533160 · Cited by 1,089 · see citing works
Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies
Diamond, A., & Sekhon, J. S. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00318 · Cited by 1,066 · see citing works
The design <i>versus</i> the analysis of observational studies for causal effects: parallels with the design of randomized trials
Rubin, D. B. (2007) · Statistics in Medicine · doi:10.1002/sim.2739 · Cited by 1,047 · see citing works
Causal Inference With General Treatment Regimes
Imai, K., & van Dyk, D. V. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000001187 · Cited by 1,022 · see citing works
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473862 · Cited by 1,021 · see citing works
Dealing with limited overlap in estimation of average treatment effects
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2009) · Biometrika · doi:10.1093/biomet/asn055 · Cited by 1,007 · see citing works
The performance of different propensity score methods for estimating marginal hazard ratios
Austin, P. C. (2012) · Statistics in Medicine · doi:10.1002/sim.5705 · Cited by 999 · see citing works
Model-Based Direct Adjustment
Rosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478441 · Cited by 998 · see citing works
Controlling Bias in Observational Studies: A Review
Cochran, W. G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.005 · Cited by 986 · see citing works
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961 · see citing works
Confounding and Collapsibility in Causal Inference
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Statistical Science · doi:10.1214/ss/1009211805 · Cited by 933 · see citing works
Semiparametric Efficiency in Multivariate Regression Models with Missing Data
Robins, J. M., & Rotnitzky, A. G. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476494 · Cited by 909 · see citing works
Matching on the Estimated Propensity Score
Abadie, A., & Imbens, G. W. (2016) · Econometrica · doi:10.3982/ecta11293 · Cited by 902 · see citing works
8. Misunderstandings Between Experimentalists and Observationalists About Causal Inference
Imai, K., King, G., & Stuart, E. A. (2008) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2007.00527.x · Cited by 895 · see citing works
Self-citation
A critical look at methods for handling missing covariates in epidemiologic regression analyses.
Greenland, S., & Finkle, W. D. (1995) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a117592 · Cited by 891 · see citing works
Improving 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 888 · see citing works
Self-citation
Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
Rubin, D. B. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10482513 · Cited by 852 · see citing works
The Consquences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment
Rosenbaum, P. R. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981697 · Cited by 849 · see citing works
Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions
Cornfield, J., Haenszel, W. M., Hammond, E., Lilienfeld, A. M., Shimkin, M. B., & Wynder, E. L. (1959) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/22.1.173 · Cited by 842 · see citing works
Causal 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 · see citing works
Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810 · see citing works
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802 · see citing works
Self-citation
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Rubin, D., & Thomas, N. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474233 · Cited by 796 · see citing works
Toward Causal Inference With Interference
Hudgens, M. G., & Halloran, M. E. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000292 · Cited by 768 · see citing works
Matching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737 · see citing works
Results of Multivariable Logistic Regression, Propensity Matching, Propensity Adjustment, and Propensity-based Weighting under Conditions of Nonuniform Effect
Kurth, T., Walker, A. M., Glynn, R. J., Chan, K. A., Gaziano, J. M., Berger, K., & Robins, J. M. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwj047 · Cited by 724 · see citing works
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 3× · see citing works
“when there is intense competition for controls, and performs well when there is little competition (Gu and Rosenbaum, 1993).”
BackgroundInfluential
Characterizing Selection Bias Using Experimental Data
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998) · doi:10.3386/w6699 · Cited by 696 · see citing works
Full Matching in an Observational Study of Coaching for the SAT
Hansen, B. B. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000647 · Cited by 648 · see citing works
Matching to Remove Bias in Observational Studies
Rubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.007 · Cited by 600 · see citing works
A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use
Austin, P. C., & Mamdani, M. (2005) · Statistics in Medicine · doi:10.1002/sim.2328 · Cited by 560 · see citing works
Bias Reduction Using Mahalanobis-Metric Matching
Rubin, D. B. (1980) · Biometrics · doi:10.2307/2529981 · Cited by 539 · see citing works
The Dangers of Extreme Counterfactuals
King, G., & Zeng, L. (2005) · Political Analysis · doi:10.1093/pan/mpj004 · Cited by 533 · see citing works
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 529 · see citing works
The Bias Due to Incomplete Matching
Rosenbaum, P. R., & Rubin, D. B. (1985) · Biometrics · doi:10.2307/2530647 · Cited by 524 · see citing works
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 · see citing works
Observational Studies
Rosenbaum, P. R. (2002) · Springer Series in Statistics · doi:10.1007/978-1-4757-3692-2_1 · Cited by 484 · see citing works
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity ScorePreprint
Hirano, K., Imbens, G. W., & Ridder, G. (2002) · SSRN Electronic Journal · doi:10.2139/ssrn.324940 · Cited by 480 · see citing works
Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments
Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000733 · Cited by 479 · see citing works
What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference
Sobel, M. E. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000636 · Cited by 468 · see citing works
The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529685 · Cited by 456 · see citing works
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 451 · see citing works
cem : Software for Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2009) · Journal of Statistical Software · doi:10.18637/jss.v030.i09 · Cited by 433 · see citing works
The Bias Due to Incomplete Matching
Rosenbaum, P. R. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.020 · Cited by 422 · see citing works
Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence
Zhao, Z. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023705 · Cited by 389 · see citing works
Matching Estimators of Causal Effects
Morgan, S. L., & Harding, D. J. (2006) · Sociological Methods & Research · doi:10.1177/0049124106289164 · Cited by 388 · see citing works
Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
Robins, J. M., Mark, S. D., & Newey, W. K. (1992) · Biometrics · doi:10.2307/2532304 · Cited by 384 · see citing works
6. Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
Smith, H. L. (1997) · Sociological Methodology · doi:10.1111/1467-9531.271030 · Cited by 381 · see citing works
The prognostic analogue of the propensity score
Hansen, B. B. (2008) · Biometrika · doi:10.1093/biomet/asn004 · Cited by 379 · see citing works
Evaluating Kindergarten Retention Policy
Hong, G., & Raudenbush, S. W. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000447 · Cited by 375 · see citing works
Interference Between Units in Randomized Experiments
Rosenbaum, P. R. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001112 · Cited by 371 · see citing works
Nonexperimental Versus Experimental Estimates of Earnings Impacts
Glazerman, S., Levy, D. M., & Myers, D. (2003) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716203254879 · Cited by 352 · see citing works
Some Practical Issues in the Evaluation of Heterogeneous Labour Market Programmes by Matching Methods
Lechner, M. (2002) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/1467-985x.0asp2 · Cited by 349 · see citing works
Effects of Misspecification of the Propensity Score on Estimators of Treatment Effect
Drake, C. (1993) · Biometrics · doi:10.2307/2532266 · Cited by 347 · see citing works
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
Rubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.030 · Cited by 344 · see citing works
Principles for modeling propensity scores in medical research: a systematic literature review
Weitzen, S. H., Lapane, K. L., Toledano, A. Y., Hume, A. L., & Mor, V. (2004) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.969 · Cited by 344 · see citing works
Using multivariate matched sampling and regression adjustment to control bias in observational studies
Rubin, D. B. (1979) · Journal of the American Statistical Association · doi:10.2307/2286330 · Cited by 341 · see citing works
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 · see citing works
Estimating and Using Propensity Scores with Partially Missing Data
D'Agostino, R., & Rubin, D. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474263 · Cited by 330 · see citing works
Using full matching to estimate causal effects in nonexperimental studies: Examining the relationship between adolescent marijuana use and adult outcomes.
Stuart, E. A., & Green, K. M. (2008) · Developmental Psychology · doi:10.1037/0012-1649.44.2.395 · Cited by 315 · see citing works
Self-citation
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 · see citing works
Matched Sampling for Causal Effects: Contributor Acknowledgments
Rubin, D. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725 · Cited by 287 · see citing works
Evaluating uses of data mining techniques in propensity score estimation: a simulation study
Setoguchi, S., Schneeweiß, S., Brookhart, M. A., Glynn, R. J., & Cook, E. F. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1555 · Cited by 273 · see citing works
Model Selection, Confounder Control, and Marginal Structural Models
Joffe, M. M., Have, T. R. T., Feldman, H. I., & Kimmel, S. E. (2004) · The American Statistician · doi:10.1198/000313004x5824 · Cited by 266 · see citing works
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 254 · see citing works
The performance of different propensity score methods for estimating marginal odds ratios
Austin, P. C. (2006) · Statistics in Medicine · doi:10.1002/sim.2781 · Cited by 249 · see citing works
Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse
Lu, B., Zanutto, E. L., Hornik, R., & Rosenbaum, P. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753381896 · Cited by 248 · see citing works
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 · see citing works
Optimal multivariate matching before randomization.
Greevy, R., Lu, B., Silber, J., & Rosenbaum, P. (2004) · Biostatistics · doi:10.1093/biostatistics/5.2.263 · Cited by 233 · see citing works
Balanced Risk Set Matching
Li, Y.-F., Propert, K., & Rosenbaum, P. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753208573 · Cited by 197 · see citing works
The Role of a Second Control Group in an Observational Study
Rosenbaum, P. R. (1987) · Statistical Science · doi:10.1214/ss/1177013232 · Cited by 197 · see citing works
Characterizing the effect of matching using linear propensity score methods with normal distributions
Rubin, D. B., & Thomas, N. (1992) · Biometrika · doi:10.1093/biomet/79.4.797 · Cited by 188 · see citing works
Multivariate Matching Methods That are Equal Percent Bias Reducing, I: Some Examples
Rubin, D. B. (1976) · Biometrics · doi:10.2307/2529342 · Cited by 167 · see citing works
Choice as an Alternative to Control in Observational Studies
Rosenbaum, P. R. (1999) · Statistical Science · doi:10.1214/ss/1009212410 · Cited by 166 · see citing works
Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer
Rosenbaum, P. R., Ross, R. N., & Silber, J. H. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001059 · Cited by 163 · see citing works
Are Experiments the Only Option? A Look at Dropout Prevention Programs
Agodini, R., & Dynarski, M. (2004) · The Review of Economics and Statistics · doi:10.1162/003465304323023741 · Cited by 162 · see citing works
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 · see citing works
Interval estimation for treatment effects using propensity score matching
Hill, J., & Reiter, J. P. (2005) · Statistics in Medicine · doi:10.1002/sim.2277 · Cited by 149 · see citing works
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Gu, X. S., & Rosenbaum, P. R. (1993) · Journal of Computational and Graphical Statistics · doi:10.2307/1390693 · Cited by 145 · see citing works
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.2307/2669923 · Cited by 139 · see citing works
Model-Based Direct Adjustment
Rosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.2307/2289440 · Cited by 138 · see citing works
Multiple Imputation for Nonresponse
Drechsler, J. (2011) · Lecture notes in statistics · doi:10.1007/978-1-4614-0326-5_5 · Cited by 135 · see citing works
Seleção de variáveis em QSAR
Ferreira, M. M. C., Montanari, C. A., & Gaudio, A. C. (2002) · Química Nova · doi:10.1590/s0100-40422002000300017 · Cited by 135 · see citing works
Matching on the Estimated Propensity Score
Abadie, A., & Imbens, G. W. (2009) · National Bureau of Economic Research · doi:10.3386/w15301 · Cited by 127 · see citing works
Affinely Invariant Matching Methods with Ellipsoidal Distributions
Rubin, D. B., & Thomas, N. (1992) · The Annals of Statistics · doi:10.1214/aos/1176348671 · Cited by 125 · see citing works
Multivariate Matching Methods That are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample Sizes
Rubin, D. B. (1976) · Biometrics · doi:10.2307/2529343 · Cited by 124 · see citing works
Developing practical recommendations for the use of propensity scores: Discussion of ‘A critical appraisal of propensity score matching in the medical literature between 1996 and 2003’ by Peter Austin, Statistics in Medicine
Stuart, E. A. (2008) · Statistics in Medicine · doi:10.1002/sim.3207 · Cited by 115 · see citing works
Self-citation
Experimental Designs in Sociological Research.
Kempthorne, O., & Chapin, F. S. (1948) · Journal of the American Statistical Association · doi:10.2307/2280283 · Cited by 115 · see citing works
A Note on Optimal Matching With Variable Controls Using the Assignment Algorithm
Ming, K., & Rosenbaum, P. (2001) · Journal of Computational and Graphical Statistics · doi:10.1198/106186001317114938 · Cited by 111 · see citing works
The Propensity Score: A Means to an EndPreprint
Augurzky, B., & Schmidt, C. Μ. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.270919 · Cited by 109 · see citing works
Semiparametric Efficiency in Multivariate Regression Models with Missing Data
Robins, J. M., & Rotnitzky, A. G. (1995) · Journal of the American Statistical Association · doi:10.2307/2291135 · Cited by 108 · see citing works
Design of Observational Studies
Bedogni, G. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_8.x · Cited by 104 · see citing works
A Comparison of Experimental and Observational Data Analyses
Hill, J., Reiter, J. P., & Zanutto, E. L. (2004) · Wiley series in probability and statistics · doi:10.1002/0470090456.ch5 · Cited by 99 · see citing works
Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach
Qu, Y., & Lipkovich, I. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3549 · Cited by 97 · see citing works
Different Methods of Balancing Covariates Leading to Different Effect Estimates in the Presence of Effect Modification
Lunt, M., Solomon, D. H., Rothman, K. J., Glynn, R., Hyrich, K. L., Symmons, D. P. M., & Stürmer‎, T. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwn391 · Cited by 93 · see citing works
Split Samples and Design Sensitivity in Observational Studies
Heller, R., Rosenbaum, P. R., & Small, D. S. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08338 · Cited by 85 · see citing works
The Computerized Construction of a Matched Sample
Althauser, R., & Rubin, D. (1970) · American Journal of Sociology · doi:10.1086/224936 · Cited by 80 · see citing works
Projecting 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 · see citing works
Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions
Rubin, D. B., & Stuart, E. A. (2006) · The Annals of Statistics · doi:10.1214/009053606000000407 · Cited by 67 · see citing works
Self-citation
CEM : software for coarsened exact matching
Iacus, S., King, G. W., & Porro, G. B. (2008) · RePEc: Research Papers in Economics · Cited by 66 · see citing works
Paired versus Two-Sample Design for a Clinical Trial of Treatments with Dichotomous Outcome: Power Considerations
Wacholder, S., & Weinberg, C. R. (1982) · Biometrics · doi:10.2307/2530059 · Cited by 58 · see citing works
Experimental Sociology
Greenwood, E. (1945) · Columbia University Press eBooks · doi:10.7312/gree91078 · Cited by 58 · see citing works
The essential role of balance tests in propensity‐matched observational studies: Comments on ‘A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003’ by Peter Austin, Statistics in Medicine
Hansen, B. B. (2008) · Statistics in Medicine · doi:10.1002/sim.3208 · Cited by 54 · see citing works
Matching when Covariables are Normally Distributed
Carpenter, R. G. (1977) · Biometrika · doi:10.1093/biomet/64.2.299 · Cited by 51 · see citing works
Experimental Sociology: A Study in Method.
Kendall, P. L., & Greenwood, E. (1945) · American Sociological Review · doi:10.2307/2085603 · Cited by 50 · see citing works
Estimating and Using Propensity Scores with Partially Missing Data
D’Agostino, R. B., & Rubin, D. B. (2000) · Journal of the American Statistical Association · doi:10.2307/2669455 · Cited by 31 · see citing works
Matching Methods for Selection of Participants for Follow-Up
Stuart, E. A., & Lalongo, N. S. (2010) · Multivariate Behavioral Research · doi:10.1080/00273171.2010.503544 · Cited by 30 · see citing works
Self-citation
Use of High-Cost Operative Procedures by Medicare Beneficiaries Enrolled in For-Profit and Not-for-Profit Health Plans
Schneider, E. C., Zaslavsky, A. M., & Epstein, A. M. (2004) · New England Journal of Medicine · doi:10.1056/nejmsa035634 · Cited by 30 · see citing works
Characterizing the Effect of Matching Using Linear Propensity Score Methods with Normal Distributions
Rubin, D. B., & Thomas, N. (1992) · Biometrika · doi:10.2307/2337235 · Cited by 29 · see citing works
Experimental Sociology: A Study in Method
Mueller, F. H., & Greenwood, E. (1945) · The American Catholic Sociological Review · doi:10.2307/3707536 · Cited by 28 · see citing works
Experimental Designs in Sociological Research.
Angell, R. C., & Chapin, F. S. (1948) · American Sociological Review · doi:10.2307/2086581 · Cited by 26 · see citing works
Experimental Sociology; A Study in Method
Brighouse, G., & Greenwood, E. (1946) · The American Journal of Psychology · doi:10.2307/1416902 · Cited by 19 · see citing works
Handling Baseline Differences and Missing Items in a Longitudinal Study of HIV Risk Among Runaway Youths
Song, J., Belin, T. R., Lee, M. B., Gao, X., & Rotheram‐Borus, M. J. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020327530029 · Cited by 13 · see citing works
Matching: Multivariate and Propensity Score Matching with Balance Optimization
Sekhon, J. S., & Saarinen, T. (2022) · doi:10.32614/cran.package.matching · Cited by 7 · see citing works
optmatch: Functions for Optimal Matching
Hansen, B. B., Fredrickson, M., Errickson, J., & Buckner, J. (2005) · doi:10.32614/cran.package.optmatch · Cited by 5 · see citing works
Multivariate Matching Methods That Are Equal Percent Bias Reducing, I: Some Examples
Rubin, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.011 · Cited by 4 · see citing works
PSAgraphics: Propensity Score Analysis Graphics
Helmreich, J. E., & Pruzek, R. M. (2007) · doi:10.32614/cran.package.psagraphics · Cited by 2 · see citing works
Matching when Covariables are Normally Distributed
Carpenter, R. G. (1977) · Biometrika · doi:10.2307/2335697 · Cited by 1 · see citing works

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Research works printed but not linked: 6

Czajka JC Hirabayashi S Little R Rubin DB 1992 Projecting from advance data using propensity modeling Journal of Business and Economic Statistics 10 117 131
Imai K King G Stuart EA 2008 Misunderstandings among experimentalists and observationalists in causal inference Journal of the Royal Statistical Society Series A 171 2 481 502
Potter FJ 1993 The effect of weight trimming on nonlinear survey estimates Proceedings of the Section on Survey Research Methods of American Statistical Association San Francisco, CA American Statistical Association
Rubin DB 2006 Matched Sampling for Causal Inference Cambridge University Press Cambridge, England
Scharfstein DO Rotnitzky A Robins JM 1999 Adjusting for non-ignorable drop-out using semiparametric non-response models Journal of the American Statistical Association 94 1096 1120
Snedecor GW Cochran WG 1980 Statistical Methods Iowa State University Press Ames, Iowa 7

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Vine, D., Ghosh, M., Wang, T., & Bakal, J. (2024) · CJC Open · doi:10.1016/j.cjco.2023.12.010 · Cited by 53 · see citing works
“59,60 Each patient was matched only once.”
Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51 · see citing works
Sleep-disordered breathing and Alzheimer's disease: A nationwide cohort study
Lee, J. E., Yang, S. W., Ju, Y. J., Ki, S. K., & Chun, K. H. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.01.086 · Cited by 53 · see citing works
Juvenile Transfer and the Specific Deterrence Hypothesis: Systematic Review and Meta‐Analysis
Zane, S. N., Welsh, B. C., & Mears, D. P. (2016) · Criminology & Public Policy · doi:10.1111/1745-9133.12222 · Cited by 48 · see citing works
Social determinants of disparities in weight among US children and adolescents.
Rossen, L. M., & Talih, M. (2014) · Annals of Epidemiology · doi:10.1016/j.annepidem.2014.07.010 · Cited by 48 · see citing works
Novel machine learning approach to identify preoperative risk factors associated with super-utilization of medicare expenditure following surgery
Hyer, J. M., Ejaz, A., Tsilimigras, D. I., Paredes, A. Z., Mehta, R., & Pawlik, T. M. (2019) · JAMA Surgery · doi:10.1001/jamasurg.2019.2979 · Cited by 49 · see citing works
The Impact of Library Resource Utilization on Undergraduate Students’ Academic Performance: A Propensity Score Matching Design
Kot, F. C., & Jones, J. L. (2015) · College & Research Libraries · doi:10.5860/crl.76.5.566 · Cited by 47 · see citing works
Childhood maltreatment increases the risk of suicide attempt in schizophrenia
Hassan, A. N., Stuart, E. A., & De Luca, V. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.05.012 · Cited by 44 · see citing works
Self-citation
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Luong, T. T., Sivarajah, U., & Weerakkody, V. (2021) · Information Systems Frontiers · doi:10.1007/s10796-019-09962-6 · Cited by 45 · see citing works
An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score
Hu, W., Chan, C. W., Zubizarreta, J. R., & Escobar, G. J. (2018) · Manufacturing & Service Operations Management · doi:10.1287/msom.2017.0658 · Cited by 44 · see citing works
Protective and vulnerability factors contributing to resilience in post-9/11 veterans with service-related injuries in postsecondary education.
Eakman, A. M., Schelly, C., & Henry, K. L. (2016) · American Journal of Occupational Therapy · doi:10.5014/ajot.2016.016519 · Cited by 35 · see citing works
EPIDEMIOLOGIC RESEARCH ON INTERPERSONAL VIOLENCE AND COMMON PSYCHIATRIC DISORDERS: WHERE DO WE GO FROM HERE?: Review: Violence and Common Psychiatric Disorders
Cerdá, M., DiGangi, J., Galea, S., & Koenen, K. (2012) · Depression and Anxiety · doi:10.1002/da.21947 · Cited by 34 · see citing works
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Trinh, M., Kalloniatis, M., Alonso-Caneiro, D., & Nivison-Smith, L. (2022) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.63.5.36 · Cited by 29 · see citing works
Risk of hip fracture associated with untreated and treated chronic hepatitis B virus infection
Byrne, D. D., Newcomb, C. W., Carbonari, D. M., Nezamzadeh, M. S., Leidl, K. B. F., Herlim, M., Yang, Y.-X., Hennessy, S., Kostman, J. R., Leonard, M. B., et al. (2014) · Journal of Hepatology · doi:10.1016/j.jhep.2014.04.001 · Cited by 31 · see citing works
Health utilities in people with chronic pain using a population-level survey and linked health care administrative data
Hogan, M.-E., Taddio, A., Katz, J., Shah, V., & Krahn, M. (2017) · Pain · doi:10.1097/j.pain.0000000000000776 · Cited by 30 · see citing works
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Unal, M., & Park, Y.-H. (2023) · Management Science · doi:10.1287/mnsc.2023.4716 · Cited by 29 · see citing works
Healthcare outcomes for patients with type 2 diabetes with and without comorbid obesity
Boye, K. S., Lage, M. J., & Terrell, K. (2020) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2020.107730 · Cited by 30 · see citing works
Greater Adherence and Persistence with Injectable Dulaglutide Compared with Injectable Semaglutide at 1-Year Follow-up: Data from US Clinical Practice
Mody, R., Manjelievskaia, J., Marchlewicz, E. H., Malik, R. E., Zimmerman, N. M., Irwin, D. E., & Yu, M. (2022) · Clinical Therapeutics · doi:10.1016/j.clinthera.2022.01.017 · Cited by 22 · see citing works
Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service
Callahan, A., Gombar, S., Cahan, E. M., Jung, K., Steinberg, E., Polony, V., Morse, K., Tibshirani, R., Hastie, T., Harrington, R., et al. (2021) · NEJM Catalyst · doi:10.1056/cat.21.0224 · Cited by 24 · see citing works
Improving Academic Performance and Retention of First-Year Biology Students through a Scalable Peer Mentorship Program
Wilton, M., Katz, D., Clairmont, A., Gonzalez-Nino, E., Foltz, K. R., & Christoffersen, R. E. (2021) · CBE—Life Sciences Education · doi:10.1187/cbe.21-02-0039 · Cited by 20 · see citing works
Spring-mediated cranioplasty versus endoscopic strip craniectomy for sagittal craniosynostosis
Lang, S.-S., Meier, P. M., Paden, W. Z., Storm, P. B., Heuer, G. G., Bartlett, S. P., Taylor, J. A., Swanson, J. W., Meara, J. G., Proctor, M., et al. (2021) · Journal of Neurosurgery: Pediatrics · doi:10.3171/2021.2.peds20983 · Cited by 19 · see citing works
Can We Establish Causality with Statistical Analyses? The Example of Epidemiology
Frick, U., & Rehm, J. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118947074.ch17 · Cited by 14 · see citing works
Location-Specific Thickness Patterns in Intermediate Age-Related Macular Degeneration Reveals Anatomical Differences in Multiple Retinal Layers
Trinh, M., Khou, V., Kalloniatis, M., & Nivison-Smith, L. (2021) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.62.13.13 · Cited by 14 · see citing works
Effectiveness of active occupational therapy in patients with acute stroke: A propensity score-weighted retrospective study
Yamakawa, S., Nagayama, H., Tomori, K., Ikeda, K., & Niimi, A. (2023) · Frontiers in Rehabilitation Sciences · doi:10.3389/fresc.2022.1045231 · Cited by 10 · see citing works
Do quality of life, anxiety, depression and acceptance improve after interdisciplinary pain rehabilitation? A multicentre matched control study of acceptance and commitment therapy-based versus cognitive–behavioural therapy-based programmes
Rivano Fischer, M., Schult, M.-L., Löfgren, M., & Stålnacke, B.-M. (2021) · Journal of International Medical Research · doi:10.1177/03000605211027435 · Cited by 6 · see citing works
“and in recommendations by Gewandter et al.(18,19) It is important to select appropriate covariates to include in the matching process.”
Background
The association of cigarette smoking with appetite, appetite-related hormones and food reward: a matched-pair cohort study
Alruwaili, A., Nayeemullah, R., Engin, B., Malaikah, S., James, L., Sanders, J. P., Thivel, D., Thackray, A. E., Stensel, D. J., King, J. A., et al. (2025) · Appetite · doi:10.1016/j.appet.2025.108194 · Cited by 6 · see citing works

Stronger Instruments and Refined Covariate Balance in an Observational Study of the Effectiveness of Prompt Admission to Intensive Care Units
Keele, L. J., Harris, S. K., Pimentel, S. D., & Grieve, R. (2018) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12437 · Cited by 15 · see citing works
Design-based inference for generalized network experiments with stochastic interventionsPreprint
Chattopadhyay, A., Imai, K., & Zubizarreta, J. R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.03268
Designing and Analyzing Randomized Experiments: Application to a Japanese Election Survey Experiment
Horiuchi, Y., Imai, K., & Taniguchi, N. (2007) · American Journal of Political Science · doi:10.1111/j.1540-5907.2007.00274.x · Cited by 101 · see citing works
Balancing Weights for Causal Inference
Cohn, E. R., Ben‐Michael, E., Feller, A., et al. (2023) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003102670-16 · Cited by 3 · see citing works
Rejective Sampling, Rerandomization and Regression Adjustment in Survey ExperimentsPreprint
Yang, Z., Qu, T., & Li, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09930 · Cited by 1 · see citing works

Authors: Stuart, E. A.
Affiliations: Johns Hopkins University (US)
Corresponding author: Stuart, Elizabeth A.
ORCID: Stuart, Elizabeth A.
Venue: Statistical Science
Publisher: Institute of Mathematical Statistics
Published: 2010-02-01
Volume: 25
Issue: 1
Pages: 1-21
ISSN: 0883-4237 (ISSN-L)
2168-8745
DOI: 10.1214/09-sts313
PMC: PMC2943670
OpenAlex: W2155163959
Cited by: 5,478
References: 157
Citations by year: 2026: 516 · 2025: 559 · 2024: 504 · 2023: 471 · 2022: 483 · 2021: 544 · 2020: 455 · 2019: 410 · 2018: 351 · 2017: 267 · 2016: 248 · 2015: 216 · 2014: 199 · 2013: 126 · 2012: 77
FWCI: 66.86
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Statistical Science (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
Project Euclid (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Harvard University
National Institutes of Health
National Institute of Mental Health (K25MH083846)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords: matching methods, causal inference, observational data, covariate balance, propensity score matching, causal effect estimation, bias mitigation

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Matching Methods for Causal Inference: A Review and a Look Forward

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