Measuring model misspecification: application to propensity score methods with complex survey data
type: Item
instance of: scholarly article
title: Measuring model misspecification: application to propensity score methods with complex survey data
zbMATH Open document ID: 1469.62098
author: D. Lenis
author: Benjamin Ackerman
author: Elizabeth A. Stuart
published in: Computational Statistics and Data Analysis
publication date: 2018-10-17T00:00:00Z
full work available at URL: pmc6034692
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 6957703
zbMATH Keywords: model misspecification
zbMATH Keywords: non-experimental study
zbMATH Keywords: propensity score matching
zbMATH Keywords: treatment on the treated weighting
zbMATH Keywords: complex survey data
zbMATH Keywords: causal inference
describes a project that uses: Survey
describes a project that uses: MatchIt
MaRDI profile type: Publication
OpenAlex ID: W2803248044
Wikidata QID: Q89492459
cites work: Large Sample Properties of Matching Estimators for Average Treatment Effects
cites work: Matching when covariables are normally distributed
cites work: Q4042441
cites work: On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
cites work: Full Matching in an Observational Study of Coaching for the SAT
cites work: A note on adapting propensity score matching and selection models to choice based samples
cites work: Causal Inference With General Treatment Regimes
cites work: Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
cites work: Comment: Performance of double-robust estimators when ``inverse probability'' weights are highly variable
cites work: Semiparametric Efficiency in Multivariate Regression Models with Missing Data
cites work: Q4272475
cites work: The central role of the propensity score in observational studies for causal effects
cites work: Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
cites work: Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
cites work: Matching methods for causal inference: a review and a look forward
DBLP publication ID: journals/csda/LenisAS18
DOI: 10.1016/J.CSDA.2018.05.003
Recommended article: Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
Recommended article: Bayesian approach for addressing differential covariate measurement error in propensity score methods
Recommended article: On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response
Recommended article: Efficient estimation in a partially specified nonignorable propensity score model
Recommended article: Effects of Variables in a Response Propensity Score Model for Survey Data Adjustment: A Simulation Study
Recommended article: A propensity score adjustment method for regression models with nonignorable missing covariates
Recommended article: Propensity score modelling in observational studies using dimension reduction methods
description: scientific article; zbMATH DE number 6957703