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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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