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Propensity Scores: A Practical Introduction Using R
Journal of MultiDisciplinary Evaluation, 2015 Background: This paper provides an introduction to propensity scores for evaluation practitioners. Purpose: The purpose of this paper is to provide the reader with a conceptual and practical introduction to propensity scores, matching using propensity ...
Antonio Olmos, Priyalatha Govindasamy
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An overview of propensity score matching methods for clustered data
Statistical Methods in Medical Research, 2022 Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure.
B. Langworthy, Yujie Wu, Molin Wang
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Multivariate Behavioral Research, 2011 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 ...
P. Austin
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A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data
Journal of Data Science, 2021 We extend propensity score methodology to incorporate survey weights from complex survey data and compare the use of multiple linear regression and propensity score analysis to estimate treatment effects in observational data from a complex survey.
Elaine L. Zanutto
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BMC Medical Research Methodology, 2016 Background Comparative performance of the traditional propensity score (PS) and high-dimensional propensity score (hdPS) methods in the adjustment for confounding by indication remains unclear.
Jason R. Guertin +3 more
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Life Cycle, 2022 Since the development of research methodology, there has always been keen interest in developing the accuracy of the research by comparing covariates.
S. Lee, K. P. Acharya
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Weight trimming and propensity score weighting.
PLoS ONE, 2011 Propensity score weighting is sensitive to model misspecification and outlying weights that can unduly influence results. The authors investigated whether trimming large weights downward can improve the performance of propensity score weighting and ...
Brian K Lee +2 more
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Theory and practice of propensity score analysis
Annals of Clinical Epidemiology, 2022 Propensity score analysis has been widely used in observational studies to make a causal inference. This study introduces three assumptions for causal inferences—conditional exchangeability, positivity, and consistency—and five steps for propensity score
Yohei Hashimoto, H. Yasunaga
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Stats, 2021 Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects.
Tingting Zhou +2 more
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Optimal Covariate Balancing Conditions in Propensity Score Estimation [PDF]
Journal of business & economic statistics, 2021 Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model.
Jianqing Fan +5 more
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