Develops shadow variable identification and SIO estimation achieving asymptotic normality and local efficiency for mediation effects under nonignorable missing confounders.
Identification, doubly robust estimation, and semiparametric efficiency theory of nonignorable missing data with a shadow variable.arXiv preprint arXiv:1509.02556, 2015
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.
citing papers explorer
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Efficient Nonparametric Inference for Mediation Analysis with Nonignorable Missing Confounders
Develops shadow variable identification and SIO estimation achieving asymptotic normality and local efficiency for mediation effects under nonignorable missing confounders.
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Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
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Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.