robustmediate: Robust Causal Mediation Analysis

In RobustMediate: Causal Mediation Analysis with Diagnostics and Sensitivity Analysis

Robust Causal Mediation Analysis

Description

Fits treatment, mediator, and outcome models for causal mediation analysis with continuous treatments using inverse probability weighting (IPW), and returns a precomputed robmedfit object for plotting and diagnostics.

Usage

robustmediate(
  treatment_formula,
  mediator_formula,
  outcome_formula,
  data,
  ref_dose = NULL,
  dose_grid = NULL,
  R = 500,
  alpha = 0.05,
  covariates = NULL,
  cluster_var = NULL,
  family_treatment = stats::gaussian(),
  family_mediator = stats::gaussian(),
  family_outcome = stats::gaussian(),
  spline_df = 4,
  evalue_seq = seq(1, 10, by = 0.25),
  rho_seq = seq(-1, 1, by = 0.05),
  verbose = TRUE
)

Arguments

treatment_formula	Formula for the treatment model (for example, X ~ Z1 + Z2).
mediator_formula	Formula for the mediator model (for example, M ~ X + Z1 + Z2).
outcome_formula	Formula for the outcome model (for example, Y ~ X + M + Z1 + Z2).
data	A data frame containing all analysis variables.
ref_dose	Reference dose value. Defaults to the sample mean of the treatment variable.
dose_grid	Numeric vector of dose values over which NDE, NIE, and TE are evaluated. Defaults to 100 evenly spaced points across the observed treatment range.
R	Number of bootstrap replicates. Default is 500.
alpha	Significance level. Default is 0.05.
covariates	Covariate names for balance diagnostics. If NULL, covariates are inferred from the treatment formula.
cluster_var	Optional clustering variable name. NULL assumes independent observations.
family_treatment	GLM family for the treatment model. Default is stats::gaussian().
family_mediator	GLM family for the mediator model. Default is stats::gaussian().
family_outcome	GLM family for the outcome model. Default is stats::gaussian().
spline_df	Degrees of freedom for spline-based effect summaries. Default is 4.
evalue_seq	Sequence of E-values used to build the sensitivity surface. Default is seq(1, 10, by = 0.25).
rho_seq	Sequence of rho values used to build the sensitivity surface. Default is seq(-1, 1, by = 0.05).
verbose	Logical; if TRUE, display progress messages.

Value

An object of class "robmedfit" containing:

models

Fitted treatment, mediator, and outcome models.

balance

Balance statistics before and after weighting.

effects

Dose-response summaries for NDE, NIE, and TE, including bootstrap intervals.

sensitivity

Bivariate E-value and rho sensitivity surface.

meditcv

Pathway-specific medITCV object from sensitivity_meditcv().

meditcv_profile

medITCV robustness profile from sensitivity_meditcv_profile().

cluster

Cluster information, or NULL if clustering was not used.

meta

Call, variable names, dose settings, bootstrap settings, and sample information.

Examples

n <- 400
Z1 <- rnorm(n)
Z2 <- rbinom(n, 1, 0.5)
X  <- 0.5 * Z1 + 0.3 * Z2 + rnorm(n)
M  <- 0.4 * X + 0.2 * Z1 + rnorm(n)
Y  <- 0.3 * X + 0.5 * M + 0.1 * Z1 + rnorm(n)
dat <- data.frame(Y, X, M, Z1, Z2)

fit <- robustmediate(
  treatment_formula = X ~ Z1 + Z2,
  mediator_formula = M ~ X + Z1 + Z2,
  outcome_formula = Y ~ X + M + Z1 + Z2,
  data = dat,
  R = 100
)

print(fit)

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