Measuring model misspecification: application to propensity score methods with complex survey data

DOI10.1016/j.csda.2018.05.003zbMath1469.62098OpenAlexW2803248044WikidataQ89492459 ScholiaQ89492459MaRDI QIDQ1796929

David Lenis, Benjamin Ackerman, Elizabeth A. Stuart

Publication date: 17 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6034692

zbMATH Keywords

causal inferencemodel misspecificationpropensity score matchingcomplex survey datanon-experimental studytreatment on the treated weighting

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Uses Software

• Survey
• MatchIt

Cites Work

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• Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• Comment: Performance of double-robust estimators when ``inverse probability weights are highly variable
• Matching methods for causal inference: a review and a look forward
• Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
• A note on adapting propensity score matching and selection models to choice based samples
• The central role of the propensity score in observational studies for causal effects
• Matching when covariables are normally distributed
• On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Semiparametric Efficiency in Multivariate Regression Models with Missing Data
• Large Sample Properties of Matching Estimators for Average Treatment Effects
• Full Matching in an Observational Study of Coaching for the SAT
• Causal Inference With General Treatment Regimes

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