Measuring model misspecification: application to propensity score methods with complex survey data
From MaRDI portal
Publication:1796929
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
causal inferencemodel misspecificationpropensity score matchingcomplex survey datanon-experimental studytreatment on the treated weighting
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- 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
This page was built for publication: Measuring model misspecification: application to propensity score methods with complex survey data
