Sensitivity analysis for causal inference using inverse probability weighting

DOI10.1002/bimj.201100042zbMath1226.62126OpenAlexW1984408929WikidataQ37183380 ScholiaQ37183380MaRDI QIDQ3100766

Changyu Shen, Ling-Ling Li, Martin C. Were, Xiaochun Li

Publication date: 21 November 2011

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3777387

zbMATH Keywords

sensitivity analysispropensity scoreuncontrolled confounding

Mathematics Subject Classification ID

Related Items (2)

Causal inferences from many experiments ⋮ Bounds on the conditional and average treatment effect with unobserved confounding factors

Cites Work

• Unnamed Item
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Bayesian inference for causal effects: The role of randomization
• Association, causation, and marginal structural models.
• Estimating Causal Treatment Effects from Longitudinal HIV Natural History Studies Using Marginal Structural Models
• Multiple-Bias Modelling for Analysis of Observational Data
• The Sign of the Bias of Unmeasured Confounding
• The central role of the propensity score in observational studies for causal effects
• Statistics and Causal Inference
• The Bias Due to Incomplete Matching
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Matching Using Estimated Propensity Scores: Relating Theory to Practice
• Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies
• Causal Inference Using Potential Outcomes

This page was built for publication: Sensitivity analysis for causal inference using inverse probability weighting