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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
Cites
The Bias Due to Incomplete Matching
Rosenbaum, P. R., & Rubin, D. B. (1985) · Biometrics · doi:10.2307/2530647 · Cited by 532
Self-citationDoes 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-citationCompeting Theories Structure Design
Rosenbaum, P. R. (2009) · Springer series in statistics · doi:10.1007/978-1-4419-1213-8_4 · Cited by 4
Self-citationTwo 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-citationUncommon 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-citationRosenbaum, P. R. (1995) · Springer series in statistics · doi:10.1007/978-1-4757-2443-1_7 · Cited by 2
Self-citationCausal Inference in Randomized Experiments
Rosenbaum, P. R. (2021) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003039648-ch1 · Cited by 2
Self-citationInternal and External Validity
(2020) · doi:10.4135/9781529692730 · Cited by 2
(2008) · Apress eBooks · doi:10.1007/978-1-4302-0523-4_18 · Cited by 1
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Items 301–400 of 1,002
Gagnon-Bartsch, J. A., & Shem-Tov, Y. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1241 · Cited by 34
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing
Dorn, J., & Guo, K. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2069572 · Cited by 32
“The specific sensitivity assumption used in this paper is the “marginal sensitivity model” of [56], which is a variant of Rosenbaum’s famous “Γ sensitivity model” [48, 45, 46] that is better suited for IPW analyses.”
MethodOn the implied weights of linear regression for causal inference
Chattopadhyay, A., & Zubizarreta, J. R. (2022) · Biometrika · doi:10.1093/biomet/asac058 · Cited by 43
Making Effects Manifest in Randomized Experiments
Bowers, J. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511921452.032 · Cited by 32
Reweighted Mahalanobis distance matching for cluster‐randomized trials with missing data
Greevy, R. A., Grijalva, C. G., Roumie, C. L., Beck, C., Hung, A. M., Murff, H. J., Liu, X., & Griffin, M. R. (2012) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3260 · Cited by 32
Using the Cross-Match Test to Appraise Covariate Balance in Matched Pairs
Heller, R., Rosenbaum, P. R., & Small, D. S. (2010) · The American Statistician · doi:10.1198/tast.2010.09210 · Cited by 32
Self-citationSmolka, K. M., Geradts, T., van der Zwan, P., & Rauch, A. (2023) · Journal of Small Business Management · doi:10.1080/00472778.2023.2237290 · Cited by 41
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Creating Protected Areas on Public Lands: Is There Room for Additional Conservation?
Arriagada, R., Echeverría, C., & Moya, D. E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148094 · Cited by 31
Wood, J., & Donnell, E. T. (2016) · Accident Analysis & Prevention · doi:10.1016/j.aap.2016.04.015 · Cited by 31
How strong is strong enough? Strengthening instruments through matching and weak instrument tests
Keele, L. J., & Morgan, J. W. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas932 · Cited by 32
Zajacova, A., & Lawrence, E. (2021) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231211021197 · Cited by 38
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BREEDING AND GENETICS SYMPOSIUM: Inferring causal effects from observational data in livestock1,2
Rosa, G. J. M., & Valente, B. D. (2012) · Journal of Animal Science · doi:10.2527/jas.2012-5840 · Cited by 31
Exact P-values for Network Interference
Athey, S. C., Eckles, D., & Imbens, G. W. (2015) · National Bureau of Economic Research · doi:10.3386/w21313 · Cited by 36
Miratrix, L. W., Weiss, M. J., & Henderson, B. (2021) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2020.1831115 · Cited by 41
Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution
Schrodt, F., Beck, M., Estopinan, J., Bowler, D. E., Fontaine, C., Gaüzère, P., Goury, R., Grenié, M., Martins, I. S., Morueta‐Holme, N., et al. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70131 · Cited by 40
Green, K. M., Doherty, E. E., Zebrak, K. A., & Ensminger, M. E. (2011) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2011.72.701 · Cited by 29
A Hospital‐Specific Template for Benchmarking its Cost and Quality
Silber, J. H., Rosenbaum, P. R., Ross, R. N., Ludwig, J. M., Wang, W., Niknam, B. A., Saynisch, P. A., Even‐Shoshan, O., Kelz, R. R., & Fleisher, L. A. (2014) · Health Services Research · doi:10.1111/1475-6773.12226 · Cited by 31
Self-citationMicrocredit and Food Security: Evidence from Rural Households in Uganda
Namayengo, F. M. M., Antonides, G., & Cecchi, F. (2017) · Journal of African Economies · doi:10.1093/jae/ejx043 · Cited by 30
Sensitivity Analysis of Linear Structural Causal Models
Cinelli, C., Kumor, D. R., Chen, B., Pearl, J., & Bareinboim, E. (2019) · International Conference on Machine Learning · Cited by 30
‘What do we have to lose?’: Local economic decline, prospect theory, and support for Brexit
Carreras, M. (2019) · Electoral Studies · doi:10.1016/j.electstud.2019.102094 · Cited by 31
Ham, D. W., & Miratrix, L. W. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1872 · Cited by 39
“We use the potential outcomes framework, and assume the Stable Unit Treatment Value As-sumption, i.e., no interference between units and no multiple forms of treatment (see Splawa-Neyman, Dabrowska and Speed (1990) for their historical introduction and Rosenbaum (2009) for an overview).”
MethodRacial Disparities in Operative Procedure Time
Silber, J. H., Rosenbaum, P. R., Ross, R. N., Even‐Shoshan, O., Kelz, R. R., Neuman, M. D., Reinke, C. E., Ludwig, J. M., Kyle, F. A., Bratzler, D. W., et al. (2013) · Anesthesiology · doi:10.1097/aln.0b013e31829101de · Cited by 30
Self-citationTitiunik, R., & Feher, A. (2017) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12293 · Cited by 28
Black Candidates and Black Turnout: A Study of Viability in Louisiana Mayoral Elections
Keele, L. J., Shah, P. R., White, I. K., & Kay, K. (2017) · The Journal of Politics · doi:10.1086/690302 · Cited by 29
Large, Sparse Optimal Matching with R package rebalance
Pimentel, S. D. (2016) · Observational Studies · doi:10.1353/obs.2016.0006 · Cited by 30
Wells, K. B., Tang, L., Carlson, G. A., & Asarnow, J. R. (2012) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2011.0074 · Cited by 28
Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies
Rosenbaum, P. R. (2016) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2016.1152971 · Cited by 28
Self-citationLim, S., Gao, Q., Stazesky, E., Singh, T. P., Harris, T. G., & Seligson, A. L. (2018) · BMC Health Services Research · doi:10.1186/s12913-017-2816-9 · Cited by 28
Li, L., & Donnell, E. T. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105691 · Cited by 28
Li, Q., Lin, J., Chi, A., & Davies, S. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106123 · Cited by 28
Income Taxes, Sorting and the Costs of Housing: Evidence from Municipal Boundaries in Switzerland*
Basten, C., Ehrlich, M. V., & Lassmann, A. (2017) · The Economic Journal · doi:10.1111/ecoj.12489 · Cited by 28
How the COVID-19 Pandemic has Changed the Game of Soccer
Link, D., & Anzer, G. (2021) · International Journal of Sports Medicine · doi:10.1055/a-1518-7778 · Cited by 28
Brnabic, A. J. M., Kelin, K., Ascher‐Svanum, H., Montgomery, W. S., KADZIOLA, Z. A., & Karagianis, J. L. (2011) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2011.02743.x · Cited by 28
Auditing Practice Style Variation in Pediatric Inpatient Asthma Care
Silber, J. H., Rosenbaum, P. R., Wang, W., Ludwig, J. M., Calhoun, S. R., Guevara, J. P., Zorc, J. J., Zeigler, A., & Even‐Shoshan, O. (2016) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2016.0911 · Cited by 28
Self-citationNonparametric Combination (NPC): A Framework for Testing Elaborate Theories
Caughey, D., Dafoe, A., & Seawright, J. (2017) · The Journal of Politics · doi:10.1086/689287 · Cited by 30
Using Qualitative Information to Improve Causal Inference
Glynn, A., & Ichino, N. (2014) · American Journal of Political Science · doi:10.1111/ajps.12154 · Cited by 28
Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect
Lin, Z., Ding, P., & Han, F. (2023) · Econometrica · doi:10.3982/ecta20598 · Cited by 41
The impact of international sanctions on innovation of target countries
Wen, J., Zhao, X., & Chang, C. (2022) · Economics and Politics · doi:10.1111/ecpo.12231 · Cited by 29
Tatangelo, M., TOMLINSON, G. A., Paterson, J. M., Ahluwalia, V., Kopp, A., Gomes, T., Bansback, N. J., & Bombardier, C. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.17053 · Cited by 27
Do Family Dinners Reduce the Risk for Early Adolescent Substance Use? A Propensity Score Analysis
Hoffmann, J. P., & Warnick, E. (2013) · Journal of Health and Social Behavior · doi:10.1177/0022146513497035 · Cited by 29
Blustein, E. C., Munn‐Chernoff, M. A., Grant, J. D., Sartor, C. E., Waldron, M., Bucholz, K. K., Madden, P. A. F., & Heath, A. C. (2015) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2015.76.852 · Cited by 27
Causal Inference and Generalization in Field Settings
West, S. G., Cham, H., & Liu, Y. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9780511996481.007 · Cited by 28
Matching Methods for Observational Studies Derived from Large Administrative Databases
Yu, R., Silber, J. H., & Rosenbaum, P. R. (2020) · Statistical Science · doi:10.1214/19-sts699 · Cited by 28
Self-citationDirectional Penalties for Optimal Matching in Observational Studies
Yu, R., & Rosenbaum, P. R. (2019) · Biometrics · doi:10.1111/biom.13098 · Cited by 26
“In parallel, near‐exact or almost exact matching uses a large penalty to match exactly as often as possible for some nominal covariate, such as gender (see Rosenbaum, 2010, §9.2).”
BackgroundInfluentialSelf-citationSaabneh, A. (2015) · European Journal of Population / Revue européenne de Démographie · doi:10.1007/s10680-015-9349-x · Cited by 26
“Because some covariates are missing, the model included indicator variables for the pattern of missing data (Rosenbaum 2010).”
MethodCase Definition and Design Sensitivity
Small, D. S., Cheng, J., Halloran, M. E., & Rosenbaum, P. R. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.820660 · Cited by 26
Self-citationFarrell, A. D., Henry, D. B., Bradshaw, C. P., & Reischl, T. M. (2016) · The Journal of Primary Prevention · doi:10.1007/s10935-016-0425-8 · Cited by 29
Sellers, M. M., Keele, L. J., Sharoky, C. E., Wirtalla, C. J., Bailey, E. A., & Kelz, R. R. (2018) · JAMA Surgery · doi:10.1001/jamasurg.2017.5449 · Cited by 26
Causality and Statistical LearningPreprint
Gelman, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.2619 · Cited by 26
Causal Inference in Accounting ResearchPreprint
Gow, I. D., Larcker, D. F., & Reiss, P. C. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2729565 · Cited by 26
Huang, W., Li, F., Liao, X., & Hu, P. (2017) · China Economic Review · doi:10.1016/j.chieco.2017.09.005 · Cited by 25
Sommer, A., Peters, A., Rommel, M., Cyrys, J., Grallert, H., Haller, D., Müller, C. L., & Bind, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010044 · Cited by 26
Feser, E. (2012) · International Regional Science Review · doi:10.1177/0160017612464051 · Cited by 24
Fogarty, C. B., Shi, P., Mikkelsen, M. E., & Small, D. S. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1138865 · Cited by 25
Optimal multilevel matching using network flows: An application to a summer reading intervention
Pimentel, S. D., Page, L. C., Lenard, M., & Keele, L. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1118 · Cited by 26
Huang, J. Y., Chen, Y., Landis, J. R., & Mahoney, K. B. (2019) · Journal of Medical Internet Research · doi:10.2196/13146 · Cited by 25
Matching on‐the‐fly: Sequential allocation with higher power and efficiency
Kapelner, A. D., & Krieger, A. Μ. (2014) · Biometrics · doi:10.1111/biom.12148 · Cited by 25
Sensitivity analysis in multiple imputation in effectiveness studies of psychotherapy
Crameri, A., von Wyl, A., Koemeda, M., Schulthess, P., & Tschuschke, V. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01042 · Cited by 24
A pilot design for observational studies: Using abundant data thoughtfully
Aikens, R. C., Greaves, D., & Baiocchi, M. (2020) · Statistics in Medicine · doi:10.1002/sim.8754 · Cited by 25
“In this form of the theorem, we clarify how the rate of convergence for the estimator from the pilot design depends on: (1) the sample size of the pilot dataset (2) the sample size of the analysis dataset, and (3) the specific method selected to fit ?̂? and ?̂? .”
BackgroundMethodInfluentialImplications of Data Anonymization on the Statistical Evidence of Disparity
Xu, H., & Zhang, N. (2022) · Management Science · doi:10.1287/mnsc.2021.4028 · Cited by 25
Faries, D. E., Peng, X., Pawaskar, M. D., Price, K., Stamey, J. D., & Seaman, J. W. (2013) · Value in Health · doi:10.1016/j.jval.2012.10.012 · Cited by 24
Permutation and randomization tests for network analysis
Fredrickson, M., & Chen, Y. (2019) · Social Networks · doi:10.1016/j.socnet.2019.08.001 · Cited by 23
Misspecified Mean Function Regression
Berk, R. A., Brown, L., Buja, A., George, E. I., Pitkin, E., Zhang, K., & Zhao, L. H. (2014) · Sociological Methods & Research · doi:10.1177/0049124114526375 · Cited by 26
Rossman, S. B., Willison, J. B., Mallik‐Kane, K., Kim, K., Debus-Sherrill, S., & Downey, P. M. (2012) · PsycEXTRA Dataset · doi:10.1037/e526732013-001 · Cited by 24
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved ConfoundingPreprint
Veitch, V., & Zaveri, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01747 · Cited by 24
Relaxing Occupational Licensing Requirements: Analyzing Wages and Prices for a Medical Service
Kleiner, M. M., Marier, A. F., Park, K. W., & Wing, C. (2014) · National Bureau of Economic Research · doi:10.3386/w19906 · Cited by 24
How Effective are Large Language Models in Generating Software Specifications?
Xie, D., Yoo, B., Jiang, N., Kim, M., Tan, L., Zhang, X., & Lee, J. S. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00014 · Cited by 77
“We employ the counterfactual method [55], [56]: a structured approach to identifying causal factors by altering one variable at a time to observe the impact on the outcome.”
Impact of the matching algorithm on the treatment effect estimate: A neutral comparison study
Heinz, P., Wendel‐Garcia, P. D., & Held, U. (2022) · Biometrical Journal · doi:10.1002/bimj.202100292 · Cited by 25
“A caliper is the maximal allowed width of the distance measure (Rosenbaum, 2010).”
BackgroundMethodInstrumented Difference-in-Differences
Ye, T., Ertefaie, A., Flory, J., Hennessy, S., & Small, D. S. (2022) · Biometrics · doi:10.1111/biom.13783 · Cited by 23
Blood donors and their changing engagement in other prosocial behaviors
Studte, S., Clément, M., Soliman, M., & Boenigk, S. (2018) · Transfusion · doi:10.1111/trf.15085 · Cited by 23
Defining Summer Gain Among Elementary Students With or at Risk for Reading Disabilities
Reed, D. K., Aloe, A. M., Reeger, A. J., & Folsom, J. S. (2019) · Exceptional Children · doi:10.1177/0014402918819426 · Cited by 22
“Crucial to our study, the approach attempts to account for selection bias and estimate causal effects (Rosenbaum, 2010; Rosenbaum & Rubin, 1983).”
BackgroundRosenbaum, P. R. (2012) · ScholarlyCommons (University of Pennsylvania) · Cited by 23
Self-citationKeller-Hamilton, B. L., Lü, B., Roberts, M. E., Berman, M. L., Root, E. D., & Ferketich, A. K. (2020) · Addictive Behaviors · doi:10.1016/j.addbeh.2020.106770 · Cited by 24
Replication and Evidence Factors in Observational Studies
Rosenbaum, P. R. (2021) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003039648 · Cited by 23
Self-citationUsing the Exterior Match to Compare Two Entwined Matched Control Groups
Rosenbaum, P. R., & Silber, J. H. (2013) · The American Statistician · doi:10.1080/00031305.2013.769914 · Cited by 24
Self-citationIsolation in the construction of natural experiments
Zubizarreta, J. R., Small, D. S., & Rosenbaum, P. R. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas770 · Cited by 22
Self-citationFox, H. E., Barnes, M. D., Ahmadia, G. N., Kao, G., Glew, L., Haisfield, K. M., Hidayat, N. I., Huffard, C. L., Katz, L. S., Mangubhai, S., et al. (2017) · Biological Conservation · doi:10.1016/j.biocon.2017.04.025 · Cited by 22
“The existing coral reef health monitoring program was adapted to align with the design requirements of impact evaluation to allow for causal inference (Box 2, Rosenbaum, 2010; Sekhon, 2009).”
MethodAn Instrumental Variables Design for the Effect of Emergency General Surgery
Keele, L. J., Sharoky, C. E., Sellers, M. M., Wirtalla, C. J., & Kelz, R. R. (2018) · Epidemiologic Methods · doi:10.1515/em-2017-0012 · Cited by 22
“In the match, we attempted to produce standardized differences of less than 0.10, the recommended threshold (Silber et al. 2001; Rosenbaum 2010).”
MethodInfluentialKarmakar, B., Small, D. S., & Rosenbaum, P. R. (2019) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2019.1584900 · Cited by 22
“3.2; Rosenbaum 2010, sec.”
InfluentialSelf-citationRajadesingan, A., Mahalingam, R., & Jurgens, D. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3239 · Cited by 28
A Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference
Puelz, D., Basse, G., Feller, A., & Toulis, P. (2021) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12478 · Cited by 22
“As mentioned earlier, power results for Fisher randomization tests is challenging because power ultimately depends on the full schedule of potential outcomes—very few results are available, even in the simple, non-conditional case (Rosenbaum, 2010).”
MethodFactors affecting mortality after coronary bypass surgery: a scoping review
Hardiman, S., Villán, Y., Conway, J., Sheehan, K. J., & Sobolev, B. (2022) · Journal of Cardiothoracic Surgery · doi:10.1186/s13019-022-01784-z · Cited by 24
“We extracted the effect of the primary risk factor from multivariable analysis from papers with well-defined study groups [20].”
MethodPATHS Equity Team Members, Isaac, M., Chartier, M., Brownell, M. D., Chateau, D. G., Nickel, N. C., Martens, P. J., Katz, A., Sarkar, J., Hu, M., et al. (2015) · BMC Public Health · doi:10.1186/s12889-015-1926-8 · Cited by 23
Hung, S., Lee, M., Chiou, W., Lee, C., Chen, Y., Lai, C., Chien, N.-C., Hsu, W., Liu, D., Su, Y., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094377 · Cited by 22
Economic Perceptions and Electoral Choices: A Design-Based Approach
Visconti, G. (2017) · Political Science Research and Methods · doi:10.1017/psrm.2017.26 · Cited by 23
Oestreicher-Singer, G., & Zalmanson, L. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1536768 · Cited by 36
Cantrell, S. C., Sampson, S. O., Perry, K. H., & Robershaw, K. L. (2022) · Literacy Research and Instruction · doi:10.1080/19388071.2022.2130117 · Cited by 21
“A general rule of thumb is that matched standardized differences should be less than 0.20 and preferably 0.10 (Rosenbaum, 2010; as cited in Pimentel et al., 2016).”
BackgroundInfluentialThe electoral risks of austerity
Ciobanu, C. (2023) · European Journal of Political Research · doi:10.1111/1475-6765.12604 · Cited by 22
“The first two analyses refer to the results based on matching and employ the solutions proposed by Rosenbaum (2010), Rosenbaum and Silber (2009), and by Blackwell (2014), while the third test is applied to the parametric results (online Appendix F.1) and uses the method proposed by Frank et al.…”
Sensitivity analysis for the generalization of experimental results
Huang, M. (2024) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnae012 · Cited by 21
A Powerful Approach to the Study of Moderate Effect Modification in Observational Studies
Lee, K., Small, D. S., & Rosenbaum, P. R. (2018) · Biometrics · doi:10.1111/biom.12884 · Cited by 22
Self-citationKeele, L. J., & Small, D. S. (2020) · The American Statistician · doi:10.1080/00031305.2020.1867638 · Cited by 22
Quantifying the Causal Effects of Conversational Tendencies
Zhang, J., Mullainathan, S., & Danescu-Niculescu-Mizil, C. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415202 · Cited by 24
“In what follows, we draw on the causal inference literature to formally examine the inference task underlying the allocation policy [3, 53].”
BackgroundMethodInfluentialCovariate Adjustment in Regression Discontinuity Designs
Cattaneo, M. D., Keele, L. J., & Titiunik, R. (2023) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003102670-8 · Cited by 21
Farys, R., & Wolbring, T. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23802 · Cited by 21
Writing a discussion section: how to integrate substantive and statistical expertise
Höfler, M., Venz, J., Trautmann, S., & Miller, R. E. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0490-1 · Cited by 21
Equivalence Testing for Regression Discontinuity Designs
Hartman, E. (2020) · Political Analysis · doi:10.1017/pan.2020.43 · Cited by 20
Lim, S., Marcus, S., Singh, T., Harris, T., & Seligson, A. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109112 · Cited by 20
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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