Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner

DOI10.1080/02664763.2021.1999398OpenAlexW3211679915WikidataQ115551546 ScholiaQ115551546MaRDI QIDQ6157150

Mi Zhou, Weixin Yao

Publication date: 19 June 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930795

zbMATH Keywords

sensitivity analysiscausal inferenceunmeasured confoundingsuper learner

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning

Cites Work

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• Improved double-robust estimation in missing data and causal inference models
• The central role of the propensity score in observational studies for causal effects
• Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Super Learner

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