Cited by 1,150Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyOptimal Experimental Design Methods

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Design of Observational Studies

Rosenbaum, Paul R.·2010·Springer Series in Statistics

DOI 10.1007/978-1-4419-1213-8

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The Bias Due to Incomplete Matching
Rosenbaum, P. R., & Rubin, D. B. (1985) · Biometrics · doi:10.2307/2530647 · Cited by 532
Self-citation
Does Cardiac Transplantation Prolong Life?
Gail, M. H. (1972) · Annals of Internal Medicine · doi:10.7326/0003-4819-76-5-815 · Cited by 124
Causal Inference in Randomized Experiments
Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_2 · Cited by 10
Self-citation
Competing Theories Structure Design
Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_4 · Cited by 4
Self-citation
Two Simple Models for Observational Studies
Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_3 · Cited by 3
Self-citation
Uncommon but Dramatic Responses to Treatment
Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_16 · Cited by 2
Self-citation
Multiple Control Groups
Rosenbaum, P. R. (1995) · Springer series in statistics · doi:10.1007/978-1-4757-2443-1_7 · Cited by 2
Self-citation
Causal Inference in Randomized Experiments
Rosenbaum, P. R. (2021) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003039648-ch1 · Cited by 2
Self-citation
Internal and External Validity
(2020) · doi:10.4135/9781529692730 · Cited by 2
Solutions to Common Problems
(2008) · Apress eBooks · doi:10.1007/978-1-4302-0523-4_18 · Cited by 1

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The uniform general signed rank test and its design sensitivity
Howard, S. R., & Pimentel, S. D. (2020) · Biometrika · doi:10.1093/biomet/asaa072 · Cited by 2
“This threshold is known as the design sensitivity and depends not only on the alternative but on the test statistic (Rosenbaum, 2010 a , § 14).”
BackgroundMethodInfluential
How do signals of academic performance vary across disciplines? Evidence from a survey experiment with university professors in Germany
Petzold, K., & Netz, N. (2024) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748925590-490 · Cited by 2
A systematic review of the methodology for examining the relationship between obstructive sleep apnea and type two diabetes mellitus
Taimah, M., Juber, N. F., Holland, P., & Brown, H. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1373919 · Cited by 2
“Finally, to ensure suf ﬁ cient and reproducible research studies, we strongly encourage the use of reporting guidelines to improve the accuracy and transparency of reporting observational studies (55, 56).”
BackgroundMethod
Balancing Weights for Estimating Treatment Effects in Educational Studies
Keele, L. J., Ben‐Michael, E., Lenard, M., & Page, L. C. (2025) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2025.2483775 · Cited by 2
“We provide a brief review of sensitivity analyses, but readers should see Rosenbaum (2010) for more details and Page et al. (2020) for a detailed example in the education research context.”
Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies
Yu, R., Karmakar, B., Vandeleest, J. J., & Schwarz, E. B. (2025) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkaf079 · Cited by 4
Stop Comparing Apples and Oranges: Matching for Better Results in Mining Software Repositories Studies
Nocera, S., Saarimäki, N., Lenarduzzi, V., Taibi, D., & Vegas, S. (2026) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3793302.3793378 · Cited by 2
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Nocera, S., Vegas, S., Scanniello, G., Penta, M. D., & Juristo, N. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787786 · Cited by 2
Abordaje Contrafáctico, Inferencia Causal y el Enfoque de la Puntuación de la Propensión. Efectos de las Remesas en la Escolarización Paraguaya
Gómez, P. S. (2014) · Empiria Revista de metodología de ciencias sociales · doi:10.5944/empiria.28.2014.12123 · Cited by 2
Relationships among Teachers’ Formal and Informal Positions and Their Incoming Student Composition
Kim, C. M., Frank, K. A., & Spillane, J. P. (2018) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146811812000304 · Cited by 3
Growing into Voting : Election Turnout among Young People and Habit Formation
Öhrvall, R. (2018) · Linköping studies in arts and science · doi:10.3384/diss.diva-150169 · Cited by 2
Population-Based Research Using Administrative Data to Evaluate Long-Term Outcomes in Burn Injury
Mason, S. A., Spiwak, R. P., & Logsetty, S. (2019) · Springer eBooks · doi:10.1007/978-3-030-18940-2_5 · Cited by 2
An Approximation Algorithm for Blocking of an Experimental Design
Karmakar, B. (2022) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12545 · Cited by 2
“Both MAPD and Algorithm 1 use the same cost function, which is the rank based robust Mahalanobis distance of the two covariates (Rosenbaum, 2010, section 8.3).”
BackgroundInfluential
Escaping from hunger before WW1: the nutritional transition and living standards in Western Europe and USA in the late nineteenth century
Gazeley, I., Holmes, R., Newell, A., Reynolds, K., & Rufrancos, H. G. (2022) · Cliometrica · doi:10.1007/s11698-022-00259-4 · Cited by 2
“To overcome this problem, we employ propensity score matching developed by Rosenbaum and Rubin (1983) (see Rosenbaum 2010; Imbens and Rubin 2015; Morgan and Winship 2015 for surveys).”
Method
Automated generation of comparator patients in the electronic medical record
Rigdon, J., Ostasiewski, B., Woelfel, K., Wiseman, K., Hetherington, T. C., Downs, S., & Kowalkowski, M. (2023) · Learning Health Systems · doi:10.1002/lrh2.10362 · Cited by 1
A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study
Li, T., Frank, K. A., & Chen, M. (2024) · Mathematics · doi:10.3390/math12030388 · Cited by 4
“For statisticians, sensitivity analysis has been considered as an essential part in causal inference [7,8,17,39–41].”
Background
Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects
Nestler, S., & Salditt, M. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02371-x · Cited by 2
“However, in the case of testing the null hypothesis of no average treatment effect, simulation studies showed that a FRT that uses rank data has good error rates across different simulation conditions (Imbens & Rubin, 2015; Rosenbaum, 2002, 2010).”
Method
Life-Sustaining Treatment and Advance Care Documentation among Chinese American ICU Decedents
Glover, A. C., Jia, Z., Waybill, K., & Vesel, T. (2024) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2024.03.025 · Cited by 2
Experimental Design for Causal Inference Through an Optimization Lens
Zhao, J. (2024) · doi:10.1287/educ.2024.0277 · Cited by 2
Combining Broad and Narrow Case Definitions in Matched Case-Control Studies: Firearms in the Home and Suicide Risk
Ye, T., Chen, K., & Small, D. S. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2024.2441519 · Cited by 2
“The bounds in Proposition 2 are exact, and their exact values can be easily obtained by convolving probability generating functions (Rosenbaum 2010, chap.”
What works in prison? Revisiting reintegration programs in Mexico
Massa, R., & Fondevila, G. (2025) · Justice, Opportunities, and Rehabilitation · doi:10.1080/10509674.2024.2443912 · Cited by 3
“Due to its capacity for dealing with outliers and rare categories (Rosenbaum, 2010), the selected method was the nearest neighbor matching using the robust rank-based Mahalanobis distance.”
Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation
Vukovich, M. M. (2016) · Digital Commons - DU (University of Denver) · Cited by 2
Irradiation enhanced risks of hospitalised pneumonopathy in lung cancer patients: a population-based surgical cohort study
Hung, S., Chen, Y., Chiou, W., Lai, C., Lee, M., Lo, Y.-C., Chen, L., Huang, L., Chien, N.-C., Li, S.-C., et al. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-015022 · Cited by 2
stratamatch: Prognostic Score Stratification Using a Pilot Design
Rachael, C. A., Rigdon, J., Lee, J., Baiocchi, M., Andrew, B. G., Chiu, P., Woo, Y.-P. J., & Jonathan, H. C. (2021) · The R Journal · doi:10.32614/rj-2021-063 · Cited by 3
Testing Mediated Effects of a Sex Education Program on Youth Sexual Activity
Birch, P. J. (2021) · Digital Commons - USU (Utah State University) · doi:10.26076/1f80-ce63 · Cited by 2
Performance study of propensity score methods against regression with covariate adjustment
Park, J.-C. (2015) · Journal of the Korean Data and Information Science Society · doi:10.7465/jkdi.2015.26.1.217 · Cited by 2
The Earlier the Better? Taking the AP in 10th Grade
Rodríguez, A., McKillip, M. E. M., & Niu, S. X. (2012) · Cited by 2
Propensity score models in observational comparative effectiveness studies: cornerstone of design or statistical afterthought?
Robinson, J. W. (2012) · Journal of Comparative Effectiveness Research · doi:10.2217/cer.12.4 · Cited by 2
“4 Rosenbaum PR. Design of Observational Studies. Springer, NY, USA (2010).”
Background
The Role of Information Costs in Voter Turnout
Keele, L. J., & White, I. K. (2011) · Cited by 2
The Futility of Propensity Score Methods in a Statewide Study of International Baccalaureate (IB).
May, H., Rodríguez, A., Sirinides, P. M., Perna, L. W., Yee, A. L., & Ransom, T. (2014) · Society for Research on Educational Effectiveness · Cited by 2
Komplementäre Studiendesigns zur Evidenzbasierung in der Bildungswissenschaft
Kulawiak, P. R., & Wilbert, J. (2017) · Springer eBooks · doi:10.1007/978-3-662-55377-0_2 · Cited by 2
Statistical Inference for Causal Effects in Clinical Psychology
Mozer, R., Rubin, D. B., & Zubizarreta, J. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781316995808.038 · Cited by 2
Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions
Dong, H., & Millimet, D. L. (2020) · Journal of risk and financial management · doi:10.2139/ssrn.3734761 · Cited by 2
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
Xu, Y., Mahajan, D., Manrao, L., Sharma, A., & Kıcıman, E. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441821 · Cited by 5
“There is past work on validating assumptions of causal models to the extent possible, often by exploring the sensitivity of the obtained estimate to unobserved variables [8, 24, 25].”
Background
The Designed Bootstrap for Causal Inference in Big Observational Data
Zhang, Y., & Sabbaghi, A. (2021) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-021-00213-z · Cited by 2
Multiply robust Bayesian procedures for causal inference problems
Gochanour, B. R., Chen, S., & Beebe, L. A. (2022) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2022.2101065 · Cited by 2
“For more comprehensive discussion of observational studies, see Altmann (1974), McKinlay (1975), and Rosenbaum, Rosenbaum, and Briskman (2010), among others.”
Causal Inference–Based Study of Key Contributors to Industrial Accidents
Okki, S., Chebila, M., & Nait-Said, R. (2023) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.rueng-1116 · Cited by 3
Foundations of Causal ML
Kummerfeld, E., Andrews, B., & Ma, S. (2024) · Health informatics · doi:10.1007/978-3-031-39355-6_4 · Cited by 2
Economic Analysis and Policy Studies: Special Challenges in the Prevention Sciences
Caulkins, J. P., Nicosia, N., & Pacula, R. L. (2014) · Springer eBooks · doi:10.1007/978-1-4899-7424-2_25 · Cited by 2
Die Messung der Durchsetzung informeller Normen im Vignetten- und Feldexperiment
Petzold, K., & Eifler, S. (2020) · Kriminalität und Gesellschaft · doi:10.1007/978-3-658-27228-9_7 · Cited by 2
Emissions trading and technological change
Calel, R. A. (2013) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · Cited by 2
Translating into Votes: The Electoral Impact of Spanish-Language BallotsPreprint
Hopkins, D. J. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1434374 · Cited by 2
SMART Designs in Observational Studies of Opioid Therapy Duration
Jackson, J. W., & Gagne, J. J. (2014) · Journal of General Internal Medicine · doi:10.1007/s11606-013-2725-5 · Cited by 2
Protocol for an Observational Study on the Effects of Playing High School Football on Later Life Cognitive Functioning and Mental HealthPreprint
Deshpande, S. K., Hasegawa, R. B., Rabinowitz, A. R., Whyte, J., Roan, C., Tabatabaei, A., Baiocchi, M., Karlawish, J. H., Master, C. L., & Small, D. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.01756 · Cited by 2
Design‐ and Model‐Based Analysis of Propensity Score Designs
Steiner, P. M. (2016) · Wiley series in probability and statistics · doi:10.1002/9781118947074.ch14 · Cited by 2
Propensity score based methods for estimating the treatment effects based on observational studies.
Abdia, Y. (2016) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/2504 · Cited by 2
Critical Evaluation of Action 14 Recommendations and the Suggested Way Forward for SingaporePreprint
Coronado, L., & van Staden, J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2993468 · Cited by 2
When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural ExperimentsPreprint
Lederrey, G., & West, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06578 · Cited by 2
Survey Sampling and Propensity Score Matching
Lü, B., & Lemeshow, S. A. (2018) · doi:10.1002/9781118489772.ch4 · Cited by 2
Generalized full matching and extrapolation of the results from a large-scale voter mobilization experimentPreprint
Sävje, F., Higgins, M., & Sekhon, J. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.03882 · Cited by 3
“One approach omitted from thisdiscussion,whichmany investigators finduseful, is permutation-based inference (Rosenbaum 2002, 2010).”
Background
Local Randomization and Beyond for Regression Discontinuity Designs: Revisiting a Causal Analysis of the Effects of University Grants on Dropout RatesPreprint
Branson, Z., & Mealli, F. (2018) · arXiv (Cornell University) · Cited by 2
A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in U.S. childrenPreprint
Zhang, B., & Small, D. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00215 · Cited by 2
“In a matched observational study, subjects with comparable measured confounders are put into matched sets and inferences are drawn based on comparisons within these matched sets (Rosenbaum, 2002, 2010a; Hansen, 2004; Stuart, 2010; Lu et al., 2011; Zubizarreta, 2012; Pimentel et al., 2015).”
Background
Effect of Stress on Later-Life Health: Evidence from the Vietnam War Draft
Cawley, J., de Walque, D., & Grossman, D. S. (2017) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-8063 · Cited by 2
Long-Term Study of PROSPERA on Intergenerational Occupational Mobility
Yaschine, I., Chanes, D. V., Huffman, C., Carreno, H., Hernandez, U., & Mendoza, T. (2019) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-8999 · Cited by 2
Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical OutcomesPreprint
Fogarty, C. B., Lee, K., Kelz, R. R., & Keele, L. J. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.6084/m9.figshare.13373996 · Cited by 3
The Local Randomization Framework for Regression Discontinuity Designs: A Review and Some ExtensionsPreprint
Branson, Z., & Mealli, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02761 · Cited by 11
Multivariate one-sided testing in matched observational studies as an adversarial game
Cohen, P., Olson, M. A., & Fogarty, C. B. (2020) · Biometrika · doi:10.1093/biomet/asaa024 · Cited by 2
Younger adults’ retirement saving and wealth accumulation in Britain a quantitative investigation
Suh, E. (2020) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · Cited by 2
Generalized Quantile Treatment Effect: A Flexible Bayesian Approach Using Quantile Ratio SmoothingPreprint
Venturini, S., Dominici, F., & Parmigiani, G. (2016) · Cited by 2
Statistical Matching and Subclassification with a Continuous Dose: Characterization, Algorithms, and InferencePreprint
Zhang, B., MacKay, E. J., & Baiocchi, M. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.30010.41926 · Cited by 2
Recruitment, effort, and retention effects of performance contracts for civil servants: Experimental evidence from Rwandan primary schoolsPreprint
Leaver, C., Ozier, O. W., Serneels, P. M., & Zeitlin, A. D. (2021) · doi:10.36934/wecon:2021-04 · Cited by 2
LACK OF ASSOCIATION BETWEEN CONVALESCENT PLASMA ADMINISTRATION AND LENGTH OF HOSPITAL STAY: A HOSPITAL-DAY STRATIFIED MULTI-CENTER RETROSPECTIVE COHORT STUDYPreprint
Alamgir, J., Abid, M. R., Garibaldi, B. T., Munir, N., Setoguchi, S., Hong, S., Chen, X., Kocis, P. T., Yajima, M., Alexander, G. C., et al. (2021) · medRxiv · doi:10.1101/2021.05.04.21256627 · Cited by 2
Assessing External ValidityPreprint
Bo, H., & Galiani, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474280 · Cited by 2
Causal Inference
Geraldo, P., & Brand, J. E. (2020) · Oxford University Press eBooks · doi:10.1093/obo/9780199756384-0240 · Cited by 2
Messaging without a Message: Executive Value and Social Media ActivityPreprint
Gao, R., Hilary, G., & Wang, R. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2966559 · Cited by 2
Patterns in the sequential treatment of rheumatoid arthritis patients starting a b/tsDMARD: 10-year experience from a US-based registryPreprint
Matsson, A., Solomon, D. H., Crabtree, M. M., Harrison, R. W., Litman, H. J., & Johansson, F. (2023) · Research Square · doi:10.21203/rs.3.rs-2624931/v1 · Cited by 2
Fundamentals of Causal Inference: With R
Ye, T. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2287599 · Cited by 2
Sustainability of Urban Regions and Migration in Pakistan: A GIS Analysis
Nazeer, M., Alam, S., & Fatima, A. (2021) · International Journal of Economic and Environmental Geology · doi:10.46660/ijeeg.v12i3.59 · Cited by 3
The Drowning-Out Effect: Voter Turnout and Protests
Kikuta, K. (2024) · Political Behavior · doi:10.1007/s11109-024-09941-x · Cited by 1
Up-front matching: an ongoing recruitment method for prospective observational studies that mimics randomization for selected baseline covariates
Olson, W. H., & Turkoz, I. (2024) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2024.2373436 · Cited by 1
“Note that the propensity score will be used here for its balancing properties (Rosenbaum 2010; Rosenbaum and Rubin 1983) and not as a means to create treatment ignorability as the basis for causal inference; study outcomes play no role in the up-front matching method.”
A Matched Design for Causal Inference With Survey Data: Evaluation of Medical Marijuana Legalization in Kentucky and Tennessee
Benedetti, M. H., Lü, B., & Zhu, M. (2024) · Biometrical Journal · doi:10.1002/bimj.70012 · Cited by 1
“Detailed discussion of sensitivity analysis is presented in Chapter 3 of Rosenbaum (2010).”
Causal Reasoning and Inference in Epidemiology
Didelez, V. (2024) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_74-1 · Cited by 1
Causal interaction and effect modification: a randomization-based approach to inference
Lee, Z., & Lee, K. (2025) · Journal of the Korean Statistical Society · doi:10.1007/s42952-025-00313-7 · Cited by 1
“As a nonparametric approach, randomization inference is particularly powerful because it does not rely on asymptotic assumptions or parametric model specifications (Rosenbaum, 2010), making it well-suited for finite samples and complex data structures.”
Local Ecological Knowledge, Reforestation, and Vegetation Recovery: A Remote Sensing Based Assessment in Gandaki Province, Nepal
Takahashi, T., & Soma, T. (2025) · Mountain Research and Development · doi:10.1659/mrd.2024.00015 · Cited by 1
Resilience Trajectories Among Older Adults With Disabilities: A Longitudinal Study in Anhui, China
Qiu, Y., Bi, J., Li, S., & Cong, Z. (2025) · Research on Aging · doi:10.1177/01640275251339996 · Cited by 1
“Additionally, we employed the Rosenbaum bounds method to assess the sensitivity of the average treatment effect on the treated estimates to unobservable variables (Rosenbaum, 2010).”
Climate-smart technology adoption in hazard-prone areas: does this adoption impact farm families’ food security?
Qian, J., Rahman, A., Zhu, N., & Alnafissa, M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04954-1 · Cited by 1
Assessing the impact of municipal mergers on local public expenditure dynamics in Italy
Caporali, C., Scotti, F., Baiocchetti, G., & Faggian, A. (2025) · Regional Studies · doi:10.1080/00343404.2025.2594502 · Cited by 2
AI literacy: ai4Ev, a guide for adults non-technical learners
Falco, M. (2026) · Iran Journal of Computer Science · doi:10.1007/s42044-025-00377-y · Cited by 2
Promoting anti-gang attitudes and beliefs in high school students: an evaluation of the End Gang Life program
Wong, J. S., & Beck, N. (2026) · Child & Youth Care Forum · doi:10.1007/s10566-026-09931-6 · Cited by 1
Production Gaps Among Farmers With Disabilities Are Associated With Limited Technology
Tsiboe, F., Martey, E., & Asravor, J. (2026) · Agricultural Economics · doi:10.1111/agec.70115 · Cited by 1
The Power of Prognosis: Improving Covariate Balance Tests with Outcome Information
Bicalho, C., Bouyamourn, A. Z. A., & Dunning, T. (2026) · Political Analysis · doi:10.1017/pan.2026.10050 · Cited by 5
“Testing itself is complementary to other objectives, including optimization of observed balance to estimate treatment effects as well as sensitivity analysis (Rosenbaum 2010).”
TREATMENT HETEROGENEITY AND POTENTIAL OUTCOMES IN LINEAR MIXED EFFECTS MODELS
Richardson, T., & Gadbury, G. L. (2012) · Conference on Applied Statistics in Agriculture · doi:10.4148/2475-7772.1037 · Cited by 1
Resolving scientific controversy over smelter risks and neurodegenerative effects of metals
Thomas, R. L., & Shrader‐Frechette, K. (2015) · AIMS environmental science · doi:10.3934/environsci.2015.1.56 · Cited by 1
Does long-term partial sodium channel blockade alter disease progression in MS? Evidence from a retrospective study
Counihan, T. J., Duignan, J. A., Gormley, G., Saidha, S., Dooley, C., & Newell, J. (2013) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-013-1042-7 · Cited by 1
Anger and Political Information: Evidence from Digital-Age Field Experiments ∗
Ryan, T. J. (2011) · Cited by 1
Comment on ‘What to do instead of significance testing? Calculating the “number of counterfactual cases needed to disturb a finding”’ by Stephen Gorard and Jonathan Gorard
Kuha, J., & Sturgis, P. (2016) · International Journal of Social Research Methodology · doi:10.1080/13645579.2015.1126495 · Cited by 1
A Promoting EFL Learners’ Linguistic Competence via Videogame Play-Trace Effects: An Empirical Study
Khidhir, R. J., & Wali, H. A. (2019) · Journal of Raparin University · doi:10.26750/vol(6).no(1).paper5 · Cited by 1
Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial
Keele, L. J., O’Neill, S., & Grieve, R. (2021) · Medical Decision Making · doi:10.1177/0272989x20986545 · Cited by 2
The Case-Control Approach Can be More Powerful for Matched Pair Observational Studies When the Outcome is Rare
Wang, W., Small, D. S., Cafri, G., & Paxton, E. W. (2021) · The American Statistician · doi:10.1080/00031305.2021.1972835 · Cited by 1
“To assess covariate balance, formal tests can also be used, namely the Wilcoxon rank sum test for continuous variables and Fisher’s exact test for binary variables (Rosenbaum 2010, p. 189).”
BackgroundMethodInfluential
Protocol: Evaluating the Effect of ACA Medicaid Expansion on 2015-2018 Mortality Through Matching and Weighting
Lycurgus, T., Mann, C. Z., & Hansen, B. B. (2021) · Observational Studies · doi:10.1353/obs.2021.0033 · Cited by 1
Internal and External Validity
Kazdin, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993647.003 · Cited by 1
Biomedical Literature Mining for Repurposing Laboratory Tests
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Authors: Rosenbaum, P. R.
Affiliations: University of Pennsylvania (US)
Corresponding author: Rosenbaum, Paul R.
ORCID: Rosenbaum, Paul R.
Venue: Springer Series in Statistics
Publisher: Springer New York
Published: 2010
ISSN: 0172-7397 (ISSN-L)
DOI: 10.1007/978-1-4419-1213-8
OpenAlex: W253646976
Cited by: 1,150
References: 12
Citations by year: 2026: 33 · 2025: 72 · 2024: 58 · 2023: 58 · 2022: 73 · 2021: 115 · 2020: 108 · 2019: 71 · 2018: 73 · 2017: 68 · 2016: 75 · 2015: 85 · 2014: 84 · 2013: 58 · 2012: 55
FWCI: 6.61
Type: book
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Springer series in statistics (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
Funders: National Science Foundation
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords: observational studies, research design, causal inference, statistical inference, confounding

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