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RobustMediate

Causal mediation analysis for continuous treatments with embedded balance diagnostics, dose-response curves, and sensitivity analysis for mediator-outcome confounding.

What it does

Function	What it gives you
`robustmediate()`	Fit treatment / mediator / outcome models, NDE/NIE/TE curves by g-computation with bootstrap CIs, and the sensitivity analysis 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
`sensitivity_paths()`	Robustness values for each path and for the indirect effect under joint-significance and Sobel tests (Cinelli & Hazlett, 2020)
`sensitivity_rho()`	Indirect effect as a function of the mediator-outcome error correlation `rho` (Imai, Keele & Yamamoto, 2010)
`plot_sensitivity()`	Plot of the indirect effect against `rho`
`diagnose()`	Formatted report with a paste-ready Results paragraph

Installation

# Development version from GitHub
# install.packages("pak")
pak::pkg_install("subirhait/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)                    # indirect effect vs rho
diagnose(fit)                            # paste into Results section

Why this package?

cobalt and WeightIt give balance diagnostics for the treatment model only, and mediation targets binary or categorical treatments. RobustMediate puts balance checks for both pathways, dose-response curves for a continuous treatment, and a sensitivity analysis into one workflow.

References

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.