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.
Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID -19 vaccine correlates of protection
2 Pith papers cite this work. Polarity classification is still indexing.
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The crumble package provides nonparametric tools for estimating natural direct/indirect effects, randomized interventional effects, and recanting-twin effects in mediation analysis, with guidance on identification assumptions and non-binary treatments illustrated via case studies.
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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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crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
The crumble package provides nonparametric tools for estimating natural direct/indirect effects, randomized interventional effects, and recanting-twin effects in mediation analysis, with guidance on identification assumptions and non-binary treatments illustrated via case studies.