Estimating a Marginal Causal Odds Ratio Subject to Confounding

DOI10.1080/03610920802200076zbMath1159.62346OpenAlexW2126606409MaRDI QIDQ3622049

Zhiwei Zhang

Publication date: 23 April 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920802200076

zbMATH Keywords

collapsibilitylogistic regressioncausal inference

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02)

Related Items (3)

On the logic of collapsibility for causal effect measures ⋮ Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets ⋮ Estimating and contextualizing the attenuation of odds ratios due to non collapsibility

Cites Work

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• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Logistic regression. A self-learning text. With contributions by Erica Rihl Pryor.
• Confounding and collapsibility in causal inference
• Weak convergence and empirical processes. With applications to statistics
• The central role of the propensity score in observational studies for causal effects
• Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates
• Inference and missing data
• Asymptotic Statistics

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