RobustMediate

Robust causal mediation analysis with embedded diagnostics, dose-response curves, pathway-specific sensitivity (medITCV), and a novel bivariate sensitivity contour.

What it does

Function	What it gives you
robustmediate()	Fit treatment / mediator / outcome models, compute IPW weights, NDE/NIE/TE curves with bootstrap CIs, and the full sensitivity surface in one call
plot_balance()	Dual love plot: covariate balance before/after weighting for both pathways simultaneously
plot_mediation()	Dose-response curves of NDE, NIE, TE with pointwise confidence bands
plot_sensitivity()	Novel 2-D robustness map: E-value x Imai rho — does not exist elsewhere in R
sensitivity_meditcv()	Pathway-specific mediation ITCV (medITCV) for a-path and b-path
plot_meditcv()	Robustness corridor plot for each pathway
sensitivity_meditcv_profile()	Minimum robustness principle + bottleneck identification
plot_meditcv_profile()	Fragility profile as confounding impact increases
fragility_table()	Publication-ready pathway decomposition table
diagnose()	Formatted report with a paste-ready Results paragraph

Installation

# Development version from GitHub
# install.packages("pak")
pak::pkg_install("causalfragility-lab/RobustMediate")

Quick start

library(RobustMediate)

fit <- robustmediate(
  treatment_formula = X ~ Z1 + Z2,
  mediator_formula  = M ~ X + Z1 + Z2,
  outcome_formula   = Y ~ X + M + Z1 + Z2,
  data = mydata,
  R    = 500
)

plot_balance(fit)                        # love plot
plot_mediation(fit)                      # NDE / NIE dose-response curve
plot_sensitivity(fit)                    # E-value x rho contour
plot(fit, type = "meditcv")             # medITCV robustness corridor
plot(fit, type = "meditcv_profile")     # fragility profile
fragility_table(fit)                     # pathway decomposition
diagnose(fit)                            # paste into Results section

Why this package?

• EValue plots E-values only
• mediation plots rho sensitivity only
• cobalt / WeightIt do love plots for treatment only

RobustMediate combines all three into one coherent workflow tailored to continuous-treatment mediation, and adds:

• The joint E-value x rho contour that exists nowhere else in R
• Pathway-specific medITCV (mediation ITCV) extending Frank (2000) to mediation
• Minimum robustness principle and bottleneck identification for indirect effects

References

• Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. Sociological Methods & Research, 29(2), 147-194.
• VanderWeele, T. J. & Ding, P. (2017). Sensitivity analysis in observational research: Introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.
• Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51-71.

Contributing

Bug reports and feature requests via GitHub Issues.

License

MIT