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“Interestingly, this score has a balancing property that states that if customers have the same propensity score, then the difference in their risk characteristics is independent of the assigned treatment, or 𝑇 𝑖 ⟂ 𝑿 𝑖 |𝜋(𝑡 𝑐 , 𝑿 𝑖 ) ∀𝑐 ∈ {1, … , 𝐶} (Guo & Fraser, 2009;Imbens, 2004;Rosenbaum, 2010).”
From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as Rosenbaum, 2010
In the citing paper
“We recognize, of course, that quasi-experimental methods such as instrumental variables and regression discontinuity designs allow for causal conclusions to be drawn from observational snapshots when investigators make strong, explicit assumptions and articulate a credible identification strategy 27-30 .”
From Quantifying the prevalence and impact of overreaching causal claims in social science (2026) · cites this paper as [27]
Citations
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The Bias Due to Incomplete Matching
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Self-citationDoes Cardiac Transplantation Prolong Life?
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Causal Inference in Randomized Experiments
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Self-citationCompeting Theories Structure Design
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Self-citationTwo Simple Models for Observational Studies
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Self-citationUncommon but Dramatic Responses to Treatment
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Self-citationCausal Inference in Randomized Experiments
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Self-citationInternal and External Validity
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Items 201–300 of 1,002
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A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies
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Self-citationA “placement of death” approach for studies of treatment effects on ICU length of stay
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Commentary: Using Potential Outcomes to Understand Causal Mediation Analysis
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Evidence factors in observational studies
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Social impacts of the Forest Stewardship Council certification in the Congo basin
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Study protocol: The back pain outcomes using longitudinal data (BOLD) registry
Jarvik, J. G., Comstock, B. A., Bresnahan, B. W., Nedeljković, S. S., Nerenz, D. R., Bauer, Z., Avins, A. L., James, K., Turner, J. A., Heagerty, P. J., et al. (2012) · BMC Musculoskeletal Disorders · doi:10.1186/1471-2474-13-64 · Cited by 46
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“This often forces social scientist to rely on observational (nonexperimental) data to investigate hypothesized causal relations and address the endogeneity concerns that emerge in that process (see Angrist & Pischke, 2009; Rosenbaum, 2010; Sobel, 2000).”
BackgroundCooper, J. D., Wang, W., Prentice, H. A., Funahashi, T. T., & Maletis, G. B. (2019) · The American Journal of Sports Medicine · doi:10.1177/0363546519878436 · Cited by 46
“To account for the follow-up time from primary ACLR to revision and enable a fair follow-up comparison between groups, a penalty was applied if a control with a shorter follow-up time was going to be matched with a case whose follow-up time was longer.(39) The Wilcoxon rank sum test was used for continuous covariates…”
MethodMatschinger, H., Heider, D., & König, H. (2020) · Das Gesundheitswesen · doi:10.1055/a-1009-6634 · Cited by 45
Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies
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“An example of a robust covariate distance is the rank-based Mahalanobis distance within propensity score calipers Rosenbaum (2010).”
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Causal inference in drug discovery and development
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Instrumental Variable Estimation with a Stochastic Monotonicity Assumption
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Propensity score methods for road safety evaluation: Practical suggestions from a simulation study
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“See D’Agostino and Rubin (2000), Mattei (2009), Rosenbaum (2010, secs.”
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Process tracing, causal inference, and civil war
Lyall, J. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139858472.010 · Cited by 44
An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score
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“…sure that there is sufficient overlap in the covariate distributions across levels of the instrumental variable, so that the predictions of the models are an interpolation and not an extrapolation; in doing so, the results will be less dependent on specific parametric assumptions (Rosenbaum 2010).”
Chang, E. H., Sugiyama, G., Smith, M. C. F., Nealon, W. H., Gross, D., Apterbach, G., Coppa, G. F., Alfonso, A., & Chung, P. J. (2019) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2019.10.030 · Cited by 44
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What You Can and Can’t Properly Do with Regression
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A causal inference approach to measure price elasticity in Automobile Insurance
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Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies
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Effect of retention in elementary grades on dropping out of school early
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The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP)
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Social Comparisons in Real Time: A Field Experiment of Residential Electricity and Water Use*
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Can Big Data Solve the Fundamental Problem of Causal Inference?
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Long‐term reduction of health care costs and utilization after epilepsy surgery
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Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics
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Covariate-adjusted Fisher randomization tests for the average treatment effect
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Causal inference with observational data in addiction research
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Variable‐ratio matching with fine balance in a study of the Peer Health Exchange
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BackgroundMethodInfluentialHow to See More in Observational Studies: Some New Quasi-Experimental Devices
Rosenbaum, P. R. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010814-020201 · Cited by 39
Self-citationFogarty, C. B., & Small, D. S. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1120675 · Cited by 36
The Effects of Legal and Extralegal Factors on Detention Decisions in US District Courts
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Testing one hypothesis twice in observational studies
Rosenbaum, P. R. (2012) · Biometrika · doi:10.1093/biomet/ass032 · Cited by 38
Self-citationCausal inference framework for generalizable safety effect estimates
Wood, J., & Donnell, E. T. (2017) · Accident Analysis & Prevention · doi:10.1016/j.aap.2017.05.001 · Cited by 38
Sensitivity Analysis and Power for Instrumental Variable Studies
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One Language, Two Meanings: Partisanship and Responses to Spanish
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Respondent Conditioning in Online Panel Surveys
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Althoff, T., Jindal, P., & Leskovec, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03053 · Cited by 42
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“One approach omitted from thisdiscussion,whichmany investigators finduseful, is permutation-based inference (Rosenbaum 2002, 2010).”
BackgroundA guide to regression discontinuity designs in medical applications
Cattaneo, M. D., Keele, L. J., & Titiunik, R. (2023) · Statistics in Medicine · doi:10.1002/sim.9861 · Cited by 39
“For an in-depth review of super-population vs Fisherian methods in the causal inference framework, see Rosenbaum 4 and Imbens and Rubin.”
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La evaluación de impacto en la práctica
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Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare
George, E. I., Ročková, V., Rosenbaum, P. R., Satopää, V. A., & Silber, J. H. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1276021 · Cited by 37
Self-citationOn Mitigating the Analytical Limitations of Finely Stratified Experiments
Fogarty, C. B. (2018) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12290 · Cited by 42
“…effect, hence reducing estimator variance through fine stratification while preserving the so-called ‘hands above the table’ analysis (Freedman, 2008; Lin, 2013); see also Cox (2007) and Rosenbaum (2010), section 6, for more on the importance of transparency for facilitating critical discussion.”
BackgroundBounds on the conditional and average treatment effect with unobserved confounding factors
Yadlowsky, S., Namkoong, H., Basu, S., Duchi, J. C., & Tian, L. (2022) · The Annals of Statistics · doi:10.1214/22-aos2195 · Cited by 37
“The design sensitivity [49, 50] of the sequence {t n} is the threshold design such that the power Q(t n = 1)! 0 for > design and the power Q(t n = 1)! 1 for < design.”
BackgroundMethodDemocratizing the Party: The Effects of Primary Election Reforms in Ghana
Ichino, N., & Nathan, N. L. (2021) · British Journal of Political Science · doi:10.1017/s0007123421000028 · Cited by 38
“We apply optimal full matching (Rosenbaum 1991; Rosenbaum 2010) to an original dataset of biographical information on all aspirants seeking nominations in these two major parties in 2016, as well as a similar dataset for Ghana’s 2012 elections.”
MethodFortin, S., Johnston, S. S., & Schuemie, M. J. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01282-1 · Cited by 37
van Kuijk, M. F., Deunk, M., Bosker, R., & Ritzema, E. S. (2015) · School Effectiveness and School Improvement · doi:10.1080/09243453.2015.1026268 · Cited by 56
Wavelet-domain regression and predictive inference in psychiatric neuroimaging
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Impacts of the government-supported investments on the economic farm performance in Austria
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Bridging Finite and Super Population Causal Inference
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Dynamic Fit Index Cutoffs for Confirmatory Factor Analysis ModelsPreprint
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Covariance Adjustments for the Analysis of Randomized Field Experiments
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Pricing for a cooler planet: An empirical analysis of the effect of taxing carbon
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Long-term survival following in-hospital cardiac arrest: A matched cohort study
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Matching Methods for Causal Inference
King, G. (2011) · Cited by 35
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Propensity score methods for causal inference: an overview
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Enns, J. E., Nickel, N. C., Chartier, M., Chateau, D., Campbell, R., Phillips-Beck, W., Sarkar, J., Burland, E., Katz, A., Santos, R., et al. (2021) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-021-03782-w · Cited by 32
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An Unconditional Prenatal Income Supplement Reduces Population Inequities In Birth Outcomes
Brownell, M. D., Nickel, N. C., Chartier, M., Enns, J. E., Chateau, D. G., Sarkar, J., Burland, E., Jutte, D. P., Taylor, C. R., & Katz, A. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.1290 · Cited by 35
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“Second, we entered these conditioning variables into the logit model to predict the propensity score (Rosenbaum, 2010), the conditional probability of receiving the treatment (i.”
MethodHsu, J. Y., Zubizarreta, J. R., Small, D. S., & Rosenbaum, P. R. (2015) · Biometrika · doi:10.1093/biomet/asv034 · Cited by 34
Self-citationYoon, Y. K., Park, D. W., Sohn, J. W., Kim, H. Y., Ys, K., Lee, C., Lee, M. S., Ryu, S. Y., Jang, H., Choi, Y. J., et al. (2016) · BMC Infectious Diseases · doi:10.1186/s12879-016-1650-8 · Cited by 34
Essential concepts of causal inference: a remarkable history and an intriguing future
Rubin, D. B. (2019) · Biostatistics & Epidemiology · doi:10.1080/24709360.2019.1670513 · Cited by 34
High-frequency traders and price informativeness during earnings announcements
Bhattacharya, N., Chakrabarty, B., & Wang, X. A. (2020) · Review of Accounting Studies · doi:10.1007/s11142-020-09550-z · Cited by 34
“Third, we perform pre-treatment placebo tests (Rosenbaum, 2010) for every one of our main findings using the same set of stocks used in our main analyses.”
MethodTrajectories of Blocked Community Members: Redemption, Recidivism and Departure
Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313638 · Cited by 32
“Tomakemeaningful comparisons between users who depart during their first block and those who don’t, hence eliminating trivial confounds, we use a matching technique inspired by causal inference methods [43].”
MethodInvasive lionfish in the Mediterranean: Low public awareness yet high stakeholder concerns
Kleitou, P., Savva, I., Kletou, D., Hall-Spencer, J. M., Antoniou, C., Christodoulides, Y., Chartosia, N., Hadjioannou, L., Dimitriou, A. C., Jiménez, C., et al. (2019) · Marine Policy · doi:10.1016/j.marpol.2019.02.052 · Cited by 38
“The application of an evaluation framework to assess impact (or performance of a management measure) of a project policy or programme can allow for statistical or observational analysis of ‘change’ that underlies interventions [50,51].”
MethodThe Long-Term Effect of Health Insurance on Near-Elderly Health and Mortality
Black, B. S., Espín‐Sánchez, J., French, E., & Litvak, K. (2017) · American Journal of Health Economics · doi:10.1162/ajhe_a_00076 · Cited by 34
Long-term benefits of full-day kindergarten: a longitudinal population-based study
Brownell, M. D., Nickel, N. C., Chateau, D. G., Martens, P. J., Taylor, C. R., Crockett, L. K., Katz, A., Sarkar, J., Burland, E., & Goh, C. Y. (2014) · Early Child Development and Care · doi:10.1080/03004430.2014.913586 · Cited by 37
Kang, H., Kreuels, B., May, J., & Small, D. S. (2016) · The Annals of Applied Statistics · doi:10.1214/15-aoas894 · Cited by 33
Dietary mycotoxin exposure and human health risks: A protocol for a systematic review
Goessens, T., Michailidis, T. M., Tesfamariam, K., Truong, N. N., Vertriest, F., Bader, Y., Saeger, S. D., Lachat, C. K., & Boevre, M. D. (2024) · Environment International · doi:10.1016/j.envint.2024.108456 · Cited by 39
Kidney Transplant Outcomes for Prior Living Organ Donors
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Wood, J., & Zhang, S. (2020) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198120966602 · Cited by 32
“As a rule, the smaller the value of standardized bias, the less biased the results are likely to be because of the observed covariates (23, 28, 39, 40).”
BackgroundMethodInfluentialThe Impact of Accounting Standards on Pension Investment Decisions
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Metadata
- 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)
- 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
- 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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