RobustMediate-package: RobustMediate: Causal Mediation Analysis with Diagnostics and...

In RobustMediate: Causal Mediation Analysis with Diagnostics and Sensitivity Analysis

RobustMediate: Causal Mediation Analysis with Diagnostics and Sensitivity Analysis

Description

RobustMediate provides a workflow for causal mediation analysis with continuous treatments using inverse probability weighting (IPW), diagnostic tools, and sensitivity analysis.

Main functions include:

• robustmediate() - Fits treatment, mediator, and outcome models and stores precomputed results for downstream plotting and reporting.

• plot_balance() - Displays covariate balance before and after weighting for both the treatment and mediator pathways using standardized mean differences.

• plot_mediation() - Plots estimated natural direct effects (NDE) and natural indirect effects (NIE) over the treatment range, with pointwise uncertainty bands.

• plot_sensitivity() - Displays a bivariate sensitivity surface based on E-values and sequential ignorability violations parameterized by rho.

• sensitivity_meditcv() - Computes pathway-specific mediation ITCV (medITCV) diagnostics based on Frank's impact threshold for a confounding variable framework.

• plot_meditcv() - Displays pathway-specific medITCV robustness corridors for the a-path and b-path.

• diagnose() - Produces a formatted diagnostic summary of balance, mediation effects, and sensitivity results.

Getting started

library(RobustMediate)

data(sim_mediation)

fit <- robustmediate(
  X ~ Z1 + Z2 + Z3,
  M ~ X + Z1 + Z2 + Z3,
  Y ~ X + M + Z1 + Z2 + Z3,
  data = sim_mediation,
  R = 500
)

plot(fit)
plot(fit, type = "balance")
plot(fit, type = "sensitivity")
plot(fit, type = "meditcv")
diagnose(fit)

Sensitivity interpretation

The E-value x rho surface is a bivariate robustness display rather than a single unified causal model. It is intended to help users examine how large different classes of unmeasured confounding would need to be to attenuate or nullify the estimated indirect effect.

The mediation ITCV (medITCV) is reported separately for the a-path and b-path. The indirect-effect summary is interpreted as a minimum-path robustness bound governed by the weaker pathway.

Author(s)

Maintainer: Subir Hait [e-mail] (ORCID)

References

Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. Sociological Methods & Research, 29(2), 147–194.

Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Psychological Methods, 15(4), 309–334.

VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: Introducing the E-value. Annals of Internal Medicine, 167(4), 268–274.

See Also

Useful links:

• https://github.com/causalfragility-lab/RobustMediate

• Report bugs at https://github.com/causalfragility-lab/RobustMediate/issues

RobustMediate documentation built on April 16, 2026, 5:08 p.m.