README

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

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?

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

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.

RobustMediate

What it does

Installation

Quick start

Why this package?

References

Contributing

License