Package: medrobust 0.4.4

medrobust: Robust Causal Mediation Analysis Under Differential Misclassification

Provides tools for conducting sensitivity analysis for causal mediation effects when the exposure or mediator is measured with differential misclassification (e.g., recall bias, outcome-dependent measurement error). Unlike existing measurement error correction methods that assume non-differential error or require validation data, 'medrobust' derives partial identification bounds that remain valid without gold-standard measurements. The package implements methods developed in Tofighi (2025) for partial identification bounds for Natural Direct Effects (NDE) and Natural Indirect Effects (NIE), data-driven falsification via testable implications, sensitivity analysis over user-specified ranges of misclassification parameters, diagnostic tools and publication-quality visualizations, bootstrap inference for confidence intervals via percentile and bias-corrected and accelerated (BCa) methods, and synthetic data generation for power analysis and methods research. The package handles both mediator misclassification and exposure misclassification within a unified framework. The partial identification approach builds on Manski (2003, ISBN:978-0387004549); confidence intervals for the partially identified bounds use the construction of Imbens and Manski (2004) <doi:10.1111/j.1468-0262.2004.00549.x>.

Authors:Davood Tofighi [aut, cre, cph]