Propensity scores: From naïve enthusiasm to intuitive understanding

DOI10.1177/0962280210394483zbMath1242.62124OpenAlexW1980027106WikidataQ39790382 ScholiaQ39790382MaRDI QIDQ2894112

James Carpenter, Alan Lucas, Ruth Morley, Elizabeth J. Williamson

Publication date: 28 June 2012

Published in: Statistical Methods in Medical Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/0962280210394483

zbMATH Keywords

stratificationmatchingconfoundinginverse probability weightingpropensity scoreobservational study

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (6)

The role of statistics in the era of big data: electronic health records for healthcare research ⋮ Causal inference in case of near‐violation of positivity: comparison of methods ⋮ A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching ⋮ Comparison of Type I error rates and statistical power of different propensity score methods ⋮ Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study ⋮ Is Early Smoking a Causal Risk Factor for Later Cognitive Impairment? A 20-year Prospective Study with Time-varying Propensity Score Matching Based on Random Intercept and Slope

Cites Work

• Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
• Confounding and collapsibility in causal inference
• The central role of the propensity score in observational studies for causal effects
• Bias Reduction Using Mahalanobis-Metric Matching
• The role of the propensity score in estimating dose-response functions
• Characterizing Selection Bias Using Experimental Data
• Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
• Propensity score modeling strategies for the causal analysis of observational data

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