Model Feedback in Bayesian Propensity Score Estimation
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Publication:4919601
DOI10.1111/j.1541-0420.2012.01830.xzbMath1272.62107OpenAlexW1694908962WikidataQ30538645 ScholiaQ30538645MaRDI QIDQ4919601
Yun Wang, Krista Watts, Robert W. Yeh, Francesca Dominici, Corwin M. Zigler, Brent A. Coull
Publication date: 14 May 2013
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3622139
Related Items (16)
The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores ⋮ Model averaged double robust estimation ⋮ Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation ⋮ On Bayesian estimation of marginal structural models ⋮ Robust Bayesian inference for big data: combining sensor-based records with traditional survey data ⋮ Estimating a causal exposure response function with a continuous error-prone exposure: a study of fine particulate matter and all-cause mortality ⋮ A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications ⋮ Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies ⋮ Generalized Propensity Score Approach to Causal Inference with Spatial Interference ⋮ Causal inference under mis-specification: adjustment based on the propensity score (with discussion) ⋮ Bayesian approach for addressing differential covariate measurement error in propensity score methods ⋮ Measurement error in spatial exposure models: study design implications ⋮ Variational inference for cutting feedback in misspecified models ⋮ Bayesian propensity score analysis for clustered observational data ⋮ A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data ⋮ Moment Conditions and Bayesian Non-Parametrics
Cites Work
- Doubly Robust Estimation in Missing Data and Causal Inference Models
- Calibrated Bayes, for statistics in general, and missing data in particular
- Matching methods for causal inference: a review and a look forward
- For objective causal inference, design trumps analysis
- Confounding and collapsibility in causal inference
- The central role of the propensity score in observational studies for causal effects
- Bayesian Effect Estimation Accounting for Adjustment Uncertainty
- Discussions
- Discussion of Adjustment Uncertainty and Propensity Scores
- Adjustment for Missing Confounders Using External Validation Data and Propensity Scores
- Bayesian Analysis of Binary and Polychotomous Response Data
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