The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
Open accessAbstract
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The study maintained an active comparator, new-user design, with propensity score matching that adjusted for baseline use of other antidiabetic drugs to minimize confounding by cotreatment.39”
From Neurodegeneration and Stroke After Semaglutide and Tirzepatide in Patients With Diabetes and Obesity (2025) · cites this paper as [39]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The impact of this implementation was evaluated by comparing clinical outcomes before and after system deployment, using propensity score matching to account for potential confounding factors43.”
From Machine learning predicts sepsis deterioration trajectories. (2026) · cites this paper as [43]
The cited source says what the claim attributes to it.
In the citing paper
“PSM is considered a robust method (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Austin, 2011
The cited source says what the claim attributes to it.
In the citing paper
“PSM is an analytic approach to reduce bias of measured confounding effects from covariates (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Austin, 2011
In the citing paper
“All of these models, including more advanced techniques such as propensity scores [46] or inverse probability weighting, rely on the assumption of no unmeasured confounding for the validity of the effect estimate.”
From COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology (2019) · cites this paper as [46]
In the citing paper
“Similarly, in cohort studies, vaccination against influenza not only protected against hospitalisation for pneumonia but also against hospitalisation for injury or trauma [46], indicating that the beneficial effect on pneumonia may have been overestimated.”
From COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology (2019) · cites this paper as [46]
In the citing paper
“There are four principal ways to use the propensity score to obtain an estimated treatment effect: matching, stratification, inverse probability weighting, and covariate adjustment (7). Each method will adjust for differences in characteristics of the intervention and control groups and, in so doing, minimize the effects of confounding.”
From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [7]
In the citing paper
“This approach adjusted for confounding from all factors shared across individuals initiating analgesic treatment with opioids and with NSAIDs (eg, indications for pain treatment)”
From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [42]
In the citing paper
“The active comparator design cannot account for unmeasured factors that differentiate opioid and NSAID initiators”
From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
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
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
Self-citationRosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
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
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Austin, P. C. (2011) · Pharmaceutical Statistics · doi:10.1002/pst.433 · Cited by 3,949
Self-citationCausal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Journal of Statistical Software
de Leeuw, J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.10 · Cited by 3,651
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. V. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 3,104
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
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,045
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
Rosenbaum, P. R. (2002) · Springer series in statistics · doi:10.1007/978-1-4757-3692-2 · Cited by 2,543
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
Austin, P. (2009) · Communications in statistics. Simulation and computation · doi:10.1080/03610910902859574 · Cited by 2,323
Self-citationUsing 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 · cited 3×
“Rubin (2001) proposed a set of criteria based on comparing the distribution of the propensity score between treated and untreated subjects in a sample to determine whether regression adjustment may inadequately eliminate bias when comparing outcomes between treatment groups.”
BackgroundMethodInfluentialPeduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(95)00048-8 · Cited by 2,154
(2009) · Cited by 1,811
Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
Shaw, G. M., Wasserman, C. R., O’Malley, C. D., Nelson, V. E., & Jackson, R. J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00011 · Cited by 1,783
Hernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,772
Lunceford, J. K., & Davidian, M. (2004) · Statistics in Medicine · doi:10.1002/sim.1903 · Cited by 1,676
Sekhon, J. S. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i07 · Cited by 1,521
Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491
(2007) · Cited by 1,428
McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.403 · Cited by 1,317
A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003
Austin, P. C. (2007) · Statistics in Medicine · doi:10.1002/sim.3150 · Cited by 1,249
Self-citationNormand, S. T., Landrum, M. B., Guadagnoli, E., Ayanian, J. Z., Ryan, T. J., Cleary, P. D., & McNeil, B. J. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00321-8 · Cited by 1,244
Design of Observational Studies
Rosenbaum, P. R. (2010) · Springer Series in Statistics · doi:10.1007/978-1-4419-1213-8 · Cited by 1,150
Counterfactuals and Causal Inference: Methods and Principles for Social Research
Morgan, S. L., & Winship, C. (2007) · doi:10.1017/cbo9780511804564 · Cited by 1,146
Austin, P. C., Grootendorst, P. V., & Anderson, G. M. (2006) · Statistics in Medicine · doi:10.1002/sim.2580 · Cited by 1,138
Self-citationRosenbaum, P., & Rubin, D. B. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01242.x · Cited by 1,128
Rubin, D. B. (2007) · Statistics in Medicine · doi:10.1002/sim.2739 · Cited by 1,047
The performance of different propensity score methods for estimating marginal hazard ratios
Austin, P. C. (2013) · Statistics in Medicine · doi:10.1002/sim.5705 · Cited by 1,006
Self-citationRosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478441 · Cited by 998
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961
Controlling Bias in Observational Studies: A Review
Cochran, W. G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725.005 · Cited by 955
8. Misunderstandings Between Experimentalists and Observationalists About Causal Inference
Imai, K., King, G., & Stuart, E. A. (2008) · Yale University Press eBooks · doi:10.1111/j.1467-985x.2007.00527.x · Cited by 925
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 920
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 733
Optimal Full Matching and Related Designs via Network Flows
Hansen, B. B., & Klopfer, S. O. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x137047 · Cited by 703
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 2×
“Full matching (Gu & Rosenbaum, 1993; Hansen, 2004; Rosenbaum, 1991) involves forming matched sets consisting of either one treated subject and at least one untreated subject or one untreated subject and at least one treated subject.”
BackgroundGail, M. H., Wieand, S., & Piantadosi, S. (1984) · Biometrika · doi:10.1093/biomet/71.3.431 · Cited by 683
Stürmer, T., Joshi, M., Glynn, R. J., Avorn, J., Rothman, K. J., & Schneeweiß, S. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.07.004 · Cited by 668
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
Austin, P. C. (2007) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2007.07.021 · Cited by 589
Self-citationThe Planning of Observational Studies of Human Populations
Cochran, W. G., & Chambers, S. P. (1965) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344179 · Cited by 569
Austin, P. C., & Mamdani, M. (2005) · Statistics in Medicine · doi:10.1002/sim.2328 · Cited by 560
Self-citationAverage 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 550
INTERPRETATION AND CHOICE OF EFFECT MEASURES IN EPIDEMIOLOGIC ANALYSES1
Greenland, S. (1987) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a114593 · Cited by 546
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
Shah, B. R., Laupacis, A., Hux, J. E., & Austin, P. C. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.10.016 · Cited by 493
Self-citationAustin, P. C. (2009) · Medical Decision Making · doi:10.1177/0272989x09341755 · Cited by 455
Self-citationCavanaugh, J. (2006) · Journal of the American Statistical Association · doi:10.1198/jasa.2006.s94 · Cited by 440
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.540480 · Cited by 424
Self-citationRare Outcomes, Common Treatments: Analytic Strategies Using Propensity Scores
Braitman, L. E., & Rosenbaum, P. R. (2002) · Annals of Internal Medicine · doi:10.7326/0003-4819-137-8-200210150-00015 · Cited by 364
Austin, P. C. (2010) · Statistics in Medicine · doi:10.1002/sim.3854 · Cited by 358
Self-citationLuellen, J. K., Shadish, W. R., & Clark, M. H. (2005) · Evaluation Review · doi:10.1177/0193841x05275596 · Cited by 353
Hernán, M. Á., Brumback, B., & Robins, J. M. (2002) · Statistics in Medicine · doi:10.1002/sim.1144 · Cited by 335
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
Austin, P. C. (2011) · Statistics in Medicine · doi:10.1002/sim.4200 · Cited by 318
Self-citationStandard Distance in Univariate and Multivariate Analysis
Flury, B. K., & Riedwyl, H. (1986) · The American Statistician · doi:10.1080/00031305.1986.10475403 · Cited by 289
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
Matched Sampling for Causal Effects: Contributor Acknowledgments
Rubin, D. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725 · Cited by 287
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
Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls
Ming, K., & Rosenbaum, P. R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00118.x · Cited by 265
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 265
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
Self-citationAustin, P. C. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1673 · Cited by 240
Self-citationAnalysis of Observational Health Care Data Using SAS
Faries, D. E., Leon, A. C., Haro, J. M., & Obenchain, R. L. (2010) · Cited by 239
Austin, P. C., Grootendorst, P. V., Normand, S. T., & Anderson, G. M. (2006) · Statistics in Medicine · doi:10.1002/sim.2618 · Cited by 230
Self-citationAustin, P. C., Mamdani, M., Stukel, T. A., Anderson, G. M., & Tu, J. V. (2005) · Statistics in Medicine · doi:10.1002/sim.2053 · Cited by 222
Self-citation6. A Diagnostic Routine for the Detection of Consequential Heterogeneity of Causal Effects
Morgan, S. L., & Todd, J. J. (2008) · Sociological Methodology · doi:10.1111/j.1467-9531.2008.00204.x · Cited by 222
The performance of different propensity-score methods for estimating relative risks
Austin, P. C. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.07.011 · Cited by 214
Self-citationThe Role of a Second Control Group in an Observational Study
Rosenbaum, P. R. (1987) · Statistical Science · doi:10.1214/ss/1177013232 · Cited by 197
Austin, P. C. (2008) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1674 · Cited by 188
Self-citationAustin, P. C. (2009) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1146 · Cited by 186
Self-citationAustin, P. C. (2008) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.108.790634 · Cited by 153
Self-citationInterval 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
Propensity score modeling strategies for the causal analysis of observational data.
Hullsiek, K. H., & Louis, T. (2002) · Biostatistics · doi:10.1093/biostatistics/3.2.179 · Cited by 147
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
Rosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.2307/2289440 · Cited by 138
Hong, G., & Yu, B. (2008) · Developmental Psychology · doi:10.1037/0012-1649.44.2.407 · Cited by 112
Ye, Y., & Kaskutas, L. (2009) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2009.03.018 · Cited by 103
Weitzen, S. H., Lapane, K. L., Toledano, A. Y., Hume, A. L., & Mor, V. (2004) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.986 · Cited by 93
Teenage Alcohol Use and Educational Attainment
Staff, J., Patrick, M. E., Loken, E., & Maggs, J. L. (2008) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2008.69.848 · Cited by 89
Standard Distance in Univariate and Multivariate Analysis
Flury, B. K., & Riedwyl, H. (1986) · The American Statistician · doi:10.2307/2684560 · Cited by 77
Wyse, A. E., Keesler, V., & Schneider, B. (2008) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146810811000901 · Cited by 50
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Rubin, D. B., & Thomas, N. (2000) · Journal of the American Statistical Association · doi:10.2307/2669400 · Cited by 46
A critical appraisal of propensity score matching in the medical literature from 1996 to 2003
Austin, P. C. (2008) · Statistics in Medicine · Cited by 30
Self-citationAustin, P. C. (2008) · Circulation Cardiovascular Quality and Outcomes · Cited by 13
Self-citationD’Agostino (1998) · doi:10.1002/(sici)1097-0258(19981015)17:19
Cited by
Sorted by citations
Benchimol, E. I., Smeeth, L., Guttmann, A., Harron, K., Moher, D., Petersen, I., Sørensen, H. T., von Elm, E., Langan, S. M., & RECORD Working Committee (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001885 · Cited by 5,749
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495
Self-citationTaquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00084-5 · Cited by 2,189
“HRs for COVID-19 diagnosis compared with the additional four index events showed more variation with time, partly reflecting the natural history of the comparator condition (appendix, pp 16–19, 36).”
Wang, X., & Cheng, Z. (2020) · CHEST · doi:10.1016/j.chest.2020.03.012 · Cited by 2,214
Taquet, M., Luciano, S., Geddes, J. R., & Harrison, P. J. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30462-4 · Cited by 1,484
Metadata
- Authors
- Austin, P. C.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Austin, Peter C.
- ORCID
- Austin, Peter C.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 2011-05-31
- Volume
- 46
- Issue
- 3
- Pages
- 399-424
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- PMC
- PMC3144483
- OpenAlex
- W2036193982
- Cited by
- 12,242
- References
- 95
- Citations by year
- 2026: 1460 · 2025: 1302 · 2024: 1187 · 2023: 1145 · 2022: 1277 · 2021: 1253 · 2020: 1065 · 2019: 850 · 2018: 781 · 2017: 628 · 2016: 560 · 2015: 353 · 2014: 221 · 2013: 122 · 2012: 28
- FWCI
- 104.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Multivariate Behavioral Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Institute for Clinical Evaluative Sciences
- Ontario Ministry of Health and Long-Term Care
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- propensity score, confounding, observational studies, propensity score matching, stratification, inverse probability of treatment weighting, covariate adjustment, regression-based methods
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 ›
Are you an author, or do you see a problem with this page? Request a review
