Design of Observational Studies

The Bias Due to Incomplete Matching

Rosenbaum, P. R., & Rubin, D. B. (1985) · Biometrics · doi:10.2307/2530647 · Cited by 532

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

Competing Theories Structure Design

Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_4 · Cited by 4

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

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

Multiple Control Groups

Rosenbaum, P. R. (1995) · Springer series in statistics · doi:10.1007/978-1-4757-2443-1_7 · Cited by 2

Causal Inference in Randomized Experiments

Rosenbaum, P. R. (2021) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003039648-ch1 · Cited by 2

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

Prognostic effect of a vasopressin V2 receptor antagonist in acute congestive heart failure patients with hypoperfusion, the wet–cold pattern

Matsushita, K., Harada, K., Miyamoto, T., Iida, K., Yamamoto, Y., Shiraishi, Y., Nagatomo, Y., Yoshino, H., Yamamoto, T., Nagao, K., et al. (2024) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-024-03745-y · Cited by 1

I Get by With a Little Help From My Friends: The Impact of a Gender-Tailored Group Mentoring Program on Peer Relationships

Bouchard, J., & Wong, J. S. (2024) · The Journal of Early Adolescence · doi:10.1177/02724316241288911 · Cited by 1

Causal inference with textual data: A quasi-experimental design assessing the association between author metadata and acceptance among ICLR submissions from 2017 to 2022

Chen, C., Zhang, J., Ye, T., Roth, D., & Zhang, B. (2024) · Journal of Causal Inference · doi:10.1515/jci-2023-0052 · Cited by 3

More power to you: Using machine learning to augment human coding for more efficient inference in text-based randomized trials

Mozer, R., & Miratrix, L. W. (2025) · The Annals of Applied Statistics · doi:10.1214/24-aoas1967 · Cited by 1

A novel bi-level clustering optimization approach to balance treatment of crash data

Ahmed, T., & Gayah, V. V. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108107 · Cited by 1

Using case description information to reduce sensitivity to bias for the attributable fraction among the exposed

Chen, K., Cheng, J., Halloran, M. E., & Small, D. S. (2025) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnaf066 · Cited by 1

Causal effect estimation on restricted mean survival time under case-cohort design via propensity score stratification

Lu, W., & Ni, A. (2025) · Lifetime Data Analysis · doi:10.1007/s10985-025-09667-w · Cited by 1

Real-world evidence for primary care: A primer on observational research

Pressentin, K. B. V., Motlhatlhedi, K., Musende, M., & Schuster, T. (2025) · African Journal of Primary Health Care & Family Medicine · doi:10.4102/phcfm.v17i2.5197 · Cited by 1

Generalized Quantile Treatment Effect

Venturini, S., Dominici, F., & Parmigiani, G. (2014) · Collection of Biostatistics Research Archive · Cited by 1

A Review of Design of Observational Studies

Stuart, E. A. (2010) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998610385399 · Cited by 1

Choosing a Sample

Gertler, P., Martínez, S., Prémand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2016) · World Bank, Washington, DC eBooks · doi:10.1596/978-1-4648-0779-4_ch15 · Cited by 1

Introduction to Causal Inference Approaches

Stuart, E. A., & Naeger, S. (2017) · Health services research · doi:10.1007/978-1-4939-6704-9_8-1 · Cited by 1

A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing

Lascano, J. E. (2021) · Digital Commons - USU (Utah State University) · doi:10.26076/a184-3eae · Cited by 1

Instrumental Variable Analysis

Baiocchi, M., Cheng, J., & Small, D. S. (2017) · Health services research · doi:10.1007/978-1-4939-6704-9_7-1 · Cited by 1

Do Top Social Apps Effect Voice Call? Evidence from Propensity Score Matching Methods

Jiang, H., Lin, M., Liu, B., Liu, H., Zeng, Y., Nai, H., Zhang, X., Zhao, X., Wen, D., & Ye, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34139-8_14 · Cited by 1

Persistence Impacts on Student Subgroups that Participate in the High Impact Practice of Service Learning

Louviere, J. (2020) · Digital Commons - USU (Utah State University) · doi:10.26076/1664-cf83 · Cited by 1

The impact of a population-based prevention program on cardiovascular events: Findings from the heart of new Ulm project

Sidebottom, A. C., Miedema, M. D., Benson, G. A., Vacquier, M. C., VanWormer, J. J., Sillah, A., Lindberg, R., Boucher, J. L., & Bradley, S. M. (2021) · American Heart Journal · doi:10.1016/j.ahj.2021.04.010 · Cited by 1

Design and Experimentation of Causal Relationship Discovery among Features of Healthcare Datasets

Sreeraman, Y., & Pandian, S. L. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2021.017256 · Cited by 1

Testing Partisan Effects on Economic Perceptions: A Panel Design Approach

Huang, C. (2018) · doi:10.6612/tjes.201811_25(2).0004 · Cited by 1

Polymatching algorithm in observational studies with multiple treatment groups

Nattino, G., Song, C. W., & Lü, B. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107364 · Cited by 1

Case-Control and Cohort Designs

Kazdin, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993647.008 · Cited by 1

Null Hypothesis Significance Testing

Kazdin, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993647.014 · Cited by 1

Estimation Uncertainty in Complex Sampling Designs

Hirschauer, N., Grüner, S., & Mußhoff, O. (2022) · SpringerBriefs in applied statistics and econometrics · doi:10.1007/978-3-030-99091-6_4 · Cited by 1

The Impact of Mobile Banking Adoption on Retail Banking in Germany

Becker, M., Stolper, O. A., & Walter, A. (2022) · Zeitschrift für das gesamte Genossenschaftswesen · doi:10.1515/zfgg-2022-0019 · Cited by 2

On Separation of Powers and Obfuscation in US Supreme Court Opinions

Lempert, D. B. (2022) · Journal of Law and Courts · doi:10.1017/jlc.2022.7 · Cited by 1

Estimating heterogeneous treatment effects for general responses

Gao, Z., & Hastie, T. J. (2025) · Biometrics · doi:10.1093/biomtc/ujaf162 · Cited by 1

Methods to control for overt and hidden biases in comparative studies

Hosman, C. (2011) · Deep Blue (University of Michigan) · Cited by 1

Experimental Design: Randomization and Social Experiments

Boruch, R. F., & Brumley, B. P. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.10502-1 · Cited by 1

Commentary on ‘How the debate about comparative effectiveness research (CER) should impact the future of clinical trials’ by Michael S. Lauer

Ellenberg, J. H. (2012) · Statistics in Medicine · doi:10.1002/sim.5399 · Cited by 1

Evaluating measures for improving farm competitiveness in the European Rural Development programme: a comparison of different matching approachesPreprint

Kirchweger, S., Eder, M., Kapfer, M., & Kantelhardt, J. (2012) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.126204 · Cited by 1

Three essays in development economics

Kuchibhotla, M. (2013) · doi:10.31274/etd-180810-3275 · Cited by 1

Targeting Violence Prone Offenders: Examination of an Intelligence-Led Policing Strategy

Lavery, T. A. (2013) · Figshare · Cited by 1

Statistical Analysis and Design of Crowdsourcing Applications

Kapelner, A. D. (2014) · ScholarlyCommons (University of Pennsylvania) · Cited by 1

Statistics and the Paperwork Reduction Act: An FTC Case StudyPreprint

Scheuren, F. J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2721855 · Cited by 1

Observational studies with unknown time of treatmentPreprint

Basse, G., Volfovsky, A., & Airoldi, E. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.04083 · Cited by 1

Hospital Mortality Rate Estimation for Public ReportingPreprint

George, E. I., Ročková, V., Rosenbaum, P. R., Satopää, V., & Silber, J. H. (2015) · arXiv (Cornell University) · Cited by 1

The Long-Term Effect of Health Insurance on Near-Elderly Health and MortalityPreprint

Black, B. S., Espín‐Sánchez, J., French, E., & Litvak, K. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2103669 · Cited by 1

A national study comparing charter and traditional public schools using propensity score analysis

Bryer, J. (2014) · doi:10.54014/h2br-2xkn · Cited by 1

Authors' reply to Ruan et al.: No pain, no bias?

Smolenski, D. J., Skopp, N. A., & Reger, M. A. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.12.001 · Cited by 1

Essays on Legislative Behavior: Evidence from State Legislatures.

Feher, A. (2015) · Deep Blue (University of Michigan) · Cited by 1

Economic analysis of innovation tax incentives in Brazil: essays on the impacts of law 11,196/05 on industrial innovation

Colombo, D. G. E. (2017) · doi:10.11606/t.12.2017.tde-01092017-171755 · Cited by 1

The Long-Run Effects of R&D Place-Based Policies: Evidence from Russian Science CitiesPreprint

Submitter, E. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3227009 · Cited by 1

Detecting Heterogeneous Treatment Effect with Instrumental VariablesPreprint

Johnson, M. L., Cao, J., & Kang, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03652 · Cited by 1

Lurking Inferential Monsters? Quantifying bias in non-experimental evaluations of school programsPreprint

Weidmann, B., & Miratrix, L. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07091 · Cited by 1

Quantifying Sufficient Randomness for Causal InferencePreprint

Knaeble, B., Osting, B., & Tshiaba, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05692 · Cited by 1

Trimmed Match Design for Randomized Paired Geo ExperimentsPreprint

Chen, A., Longfils, M., & Remy, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07060 · Cited by 1

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

Social Distancing and COVID-19: Randomization Inference for a Structured Dose-Response RelationshipPreprint

Zhang, B., Heng, S., Ye, T., & Small, D. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06917 · Cited by 1

Essays on Causal Inference in Randomized Experiments

Lin, W. T. (2013) · eScholarship (California Digital Library) · Cited by 1

Introduction to Causal Inference Approaches

Stuart, E. A., & Naeger, S. (2019) · Health services research · doi:10.1007/978-1-4939-8715-3_33 · Cited by 1

Dynamic Fit Index Cutoffs for Confirmatory Factor Analysis ModelsPreprint

McNeish, D., & Wolf, M. G. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/7tsm5 · Cited by 1

Propensity Score

Rosenbaum, P. R. (2015) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05196.pub2 · Cited by 1

Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain InjuryPreprint

Zhao, Z., Liu, R., Groner, J. I., Xiang, H., & Zhang, P. (2023) · medRxiv · doi:10.1101/2023.01.29.23285172 · Cited by 1

A Compound Model of Multiple Treatment Selection with Applications to Marginal Structural ModelingPreprint

Stein, D. W. J., D’Arinzo, L., Gaspar, F. W., Oliver, M., Fitzgerald, K., Lu, D., Piantadosi, S., Amin, A., & Webb, B. J. (2023) · medRxiv · doi:10.1101/2023.02.08.23285425 · Cited by 1

How do signals of academic performance vary across disciplines? Evidence from a survey experiment among university professors in GermanyPreprint

Petzold, K., & Netz, N. (2023) · doi:10.31235/osf.io/f39ra · Cited by 1

Impactos do Pronaf sobre a eficiência técnica da agricultura familiar no Brasil

da Cruz, N. B. (2023) · doi:10.11606/t.11.2023.tde-10042023-153027 · Cited by 1

Immigration and Nationalistic Attitudes: Panel Evidence from ChilePreprint

Vega-Mendez, C., & Visconti, G. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/u5grv · Cited by 1

DOS2: Design of Observational Studies, Companion to the Second Edition

Rosenbaum, P. R. (2019) · doi:10.32614/cran.package.dos2 · Cited by 1

Green Bonds: Financial Development or Financialization? A Firm-Level Analysis of Their Emission and Energy ImpactsPreprint

Stumphius, C., & Bezemer, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4892145 · Cited by 1

The evaluation of competitive research funding : .an application to French programs

Lanoë, M. (2018) · theses.fr (ABES) · doi:10.70675/fec8e8a0zb536z48b7zb067z92c6b59563fc · Cited by 1

Quantifying the prevalence and impact of overreaching causal claims in social science

Isch, C., Dörr, T., Fasching, N., Jennings, G., & Watts, D. J. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02553-x

Matching on-the-fly in Sequential Experiments for Higher Power and EfficiencyPreprint

Kapelner, A. D., & Krieger, A. Μ. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.4981

LSVMA : au plus deux composants pour apparier des résumés à des articles

Bestgen, Y. (2011) · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))

New Perspectives on Regression Adjustment in Causal Inference, with Applications to Educational Program Evaluation

Sales, A. (2013) · Deep Blue (University of Michigan)

Comparing heart failure and cancer caregiver satisfaction with hospice care

MacKenzie, M. A. (2014) · Scholarly Commons (University of Pennsylvania)

Methodological problems in causal inference, with reference to transitional justice

Lee, B. (2014) · Texas ScholarWorks (Texas Digital Library)

A POLICY-BASED ANALYSIS OF THE IMPACTS OF CASINO GAMBLING

Richard, B. (2014) · The Journal of Gambling Business and Economics · doi:10.5750/jgbe.v8i1.613

The Effects of classroom-based mindfulness meditation on MBA student mindfulness

Blackburn, K. F. (2015)

Enhancing the design of observational studies of community policing: using geospatial data mining to design non-experimental program evaluations

Kreis, C. (2013) · Bulletin de la Société Géographique de Liège

Instrumental Variables and Mendelian Randomization With Invalid Instruments

Kang, H. (2015) · Scholarly Commons (University of Pennsylvania)

Dr. Frazier et al. reply:

Frazier, T. W., Georgiades, S., Bishop, S. L., & Hardan, A. Y. (2014) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2014.04.002

Strategies for a Label Claim or Indication in the Context of Robust Point Estimates From Historical Data

Wang, J., White, R., & Peng, L. (2013) · Therapeutic Innovation & Regulatory Science · doi:10.1177/2168479012469945

Empirical evidence claims are a priori

Rowbottom, D. P. (2012) · Synthese · doi:10.1007/s11229-012-0087-x

Evaluating Risks from Antibacterial Medication Therapy

Meropol, S. B. (2010) · ScholarlyCommons (University of Pennsylvania)

Mistaking the Forest for the Trees: The Mistreatment of Group-level Treatments in the Study of American Politics

Rader, K. (2012) · doi:10.7916/d883404v

Matching methods for biomarker evaluation: a mapping with causal inference

Ghosh, D., & Sabel, M. S. (2015)

Exam Scores for College Course Placement

Patterson, B. F., & Ewing, M. (2013)

On Causal Inference for Ordinal Outcomes

Lu, J. (2015) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)

On Selection Bias Magnitudes

Najab, J. (2014) · doi:10.13021/mars/6081

Social Context and the Pathways to Happiness

Reece, C. D. (2014) · ScholarlyCommons (University of Pennsylvania)

Not my brother's keeper: How ethnic violence causes infighting

Arackal, S. A. (2014) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)

The Anesthesia Perspective

Hogan, K. J. (2014) · Springer eBooks · doi:10.1007/978-1-4471-4369-7_26

Confidence intervals for the treatment effect on the treated

Ferreira, J. A. (2017) · Journal of nonparametric statistics · doi:10.1080/10485252.2017.1324623

A Nonparametric Method for Predicting Survival ProbabilitiesPreprint

van der Klaauw, B., & Vriend, S. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2689517

Overpayment, Financial Distress, and Investor HorizonsPreprint

Andriosopoulos, D., Cesari, A. D., & Stathopoulos, K. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2728942

Opposition Parties and Anti-Government Protests in Comparative Perspective

Su, Y.-P. (2014) · D-Scholarship@Pitt (University of Pittsburgh)

Review Essay Causality and Statistical Learning 1

Morgan, S. L., Winship, C., & Gelman, A. (2011)

THE SURGICAL PROCEDURE FEEDBACK RUBRIC FOR ASSESSING RESIDENT PERFORMANCE IN THE OPERATING ROOM: DOES IT WORK?

Toprak, A. (2015) · QSpace (Queen's University Library)

Essays on the Effect of a Financial Crisis on the Productivity of Firms

Kim, B. K. (2013) · eScholarship (California Digital Library)

Propensity methods for multiple sclerosis: The devil is in the details

Ontaneda, D., & Love, T. E. (2016) · Multiple Sclerosis Journal · doi:10.1177/1352458516660390

Essays on Cloud Pricing and Causal Inference

Kilcioglu, C. (2016) · doi:10.7916/d8r78f9q

Randomization Inference and Principal Stratification in HIV Prevention Studies

Nolen, T. L. (2019) · ProQuest Demo Repository · doi:10.17615/g4wd-fb92

Modeling the Propensity Score with Statistical Learning

Uchihashi, K., & Kanemura, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46681-1_32

Does Education Equalize or Reproduce Inequality? Effects of College Degrees on Health Behaviors

Lawrence, E. (2015) · CU Scholar (University of Colorado Boulder)

Overcoming the Computing Barriers in Statistical Causal Inference

Zhang, K., & Chen, D. (2016) · ICSA book series in statistics · doi:10.1007/978-3-319-41259-7_7

Regression without Regrets: A modular approach to linear models in (quasi)-experiments.

Bowers, J., Panagopoulos, C., & Fredrickson, M. (2013)

The Matching methodology in agricultural economics: applications from Austria

Kirchweger, S., & Bahrs, E. (2015)

Political Science 590 Political Experiments: Design & Analysis, Part I

Bowers, J., & McConnaughy, C. M. (2012)

Does Innovation Norway’s grants have an effect? : an empirical research paper examining the effects of the establishing grant and development grants

Berhe, A., & Martens, T. (2016) · BIBSYS Brage (BIBSYS (Norway))

Big Data, Data Loss and Observation Analysis.

Frizelle, G., & Woodall, P. (2016) · ICIQ

Ch. 4: Statistical research designs for causal inference

Gilardi, F. (2012) · Cited by 1

Planning and Analysis of Observational Studies

Cochran, W. G. (1983) · Wiley series in probability and statistics · doi:10.1002/9780470316542 · Cited by 170

Effects and Associations

Rudas, T. (2018) · Springer texts in statistics · doi:10.1007/978-1-4939-7693-5_8

Design of Observational Studies by Paul R. Rosenbaum

Liski, E. P. (2010) · International Statistical Review · doi:10.1111/j.1751-5823.2010.00122_30.x · Cited by 1

Causal Inference from Observational Data: A Review of End and Means

Wold, H. (1956) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2342961 · Cited by 92