On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data | Paper Citation Record

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This paper cites Chapman & Hall/CRC, Boca Ratonn, 2020.

On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Chapman & Hall/CRC, Boca Ratonn, 2020

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data A new approach to causal inference in mortality studies with a sus- tained exposure period—application to control of the healthy worker survivor effect

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Implementation of g- computation on a simulated data set: demonstration of a causal inference technique

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Graphical models for recovering probabilistic and causal queries from missing data.Advances in Neural Information Processing Systems, 27, 2014

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Canonical causal diagrams to guide the treatment of missing data in epidemiologic studies.American journal of epidemiology, 187(12): 2705–2715, 2018

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This paper cites Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.Biometrical Journal, 66(3):2200326, 2024.

On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.Biometrical Journal, 66(3):2200326, 2024

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This paper cites canonical causal diagrams to guide the treatment of missing data in epidemiologic studies.

On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data canonical causal diagrams to guide the treatment of missing data in epidemiologic studies

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Mediation analysis with the mediator and outcome missing not at random.Journal of the American Statistical Association, 120(550):794–804, 2025

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Unresolved cited work

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This paper cites John Wiley & Sons, 2004.

On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data John Wiley & Sons, 2004

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data John Wiley & Sons, 2023

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Handling missing data when estimating causal effects with targeted maximum likelihood estimation.American Journal of Epidemi- ology, 193(7):1019–1030, 2024

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.Statistics in medicine, 29 (28):2920–2931, 2010

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data Imputation without nightmars: Graphical criteria for valid imputation of missing data

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data A cautious note on auxiliary variables that can increase bias in missing data problems.Multivariate Behavioral Research, 49(5): 443–459, 2014

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On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data The manual of cis-r.London: Institute of Psychiatry, 1992

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