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Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
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A generalization of sampling without replacement from a finite universe
Daniel G Horvitz and Donovan J Thompson · 1952
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Estimating causal effects of treatments in randomized and nonrandomized studies
Donald B Rubin · 1974
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The central role of the propensity score in observational studies for causal effects
Paul R. Rosenbaum and Donald B. Rubin · 1983
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Reducing bias in observational studies using subclassification on the propensity score
Paul R Rosenbaum and Donald B Rubin · 1984
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Constructing a control group using multivariate matched sampling methods that incorporate the propensity score
Paul R Rosenbaum and Donald B Rubin · 1985
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Why the logistic function? a tutorial discussion on probabilities and neural networks
Michael I Jordan · 1995
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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James M Robins, Miguel Angel Hernan, and Babette Brumback · 2000
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Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Doubly robust estimation in missing data and causal inference models
Heejung Bang and James M Robins · 2005
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Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
Joseph DY Kang and Joseph L Schafer · 2007
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Performance of propensity score calibration—a simulation study
Targeted maximum likelihood estimation for causal inference in observational studies
Megan S Schuler and Sherri Rose · 2017
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The integrated calibration index (ici) and related metrics for quantifying the calibration of logistic regression models
Peter C. Austin and Ewout W. Steyerberg · 2019
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An evaluation toolkit to guide model selection and cohort definition in causal inference
Yishai Shimoni, Ehud Karavani, Sivan Ravid, Peter Bak, Tan Hung Ng, Sharon Hensley Alford, Denise Meade, and Yaara Goldschmidt · 2019
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Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, and Dan Cervone · 2019
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A tutorial on calibration measurements and calibration models for clinical prediction models
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Til Stürmer, Sebastian Schneeweiss, Kenneth J Rothman, Jerry Avorn, and Robert J Glynn · 2007
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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An introduction to the augmented inverse propensity weighted estimator
Adam N Glynn and Kevin M Quinn · 2010
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Propensity score estimation: machine learning and classification methods as alternatives to logistic regression
Daniel Westreich, Justin Lessler, and Michele Jonsson Funk · 2010
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Yingxiang Huang, Wentao Li, Fima Macheret, Rodney A Gabriel, and Lucila Ohno-Machado · 2020
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A review of the use of propensity score diagnostics in papers published in high-ranking medical journals
Emily Granger, Tim Watkins, Jamie C Sergeant, and Mark Lunt · 2020
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Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study
Ben Van Calster, Maarten van Smeden, Bavo De Cock, and Ewout W Steyerberg · 2020
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To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets
Hana Šinkovec, Georg Heinze, Rok Blagus, and Angelika Geroldinger · 2021
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Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting
John Tazare, Richard Wyss, Jessica M Franklin, Liam Smeeth, Stephen JW Evans, Shirley V Wang, Sebastian Schneeweiss, Ian J Douglas, Joshua J Gagne, and Elizabeth J Williamson · 2022
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