sensitivity_pathwise: Sensitivity Analysis for Pathwise Indirect Effects

In multimedia: Multimodal Mediation Analysis

Sensitivity Analysis for Pathwise Indirect Effects

Description

For causal identification, mediation analysis relies on several untestable assumptions. One important one is that there is no confounding between the counterfactual mediator and outcome variables. Even though we can never know whether this exists, we can measure the sensitivity of our conclusions to the existence/strength of such a confounder. In this function, we approach this by inducing (unallowable) correlation between the mediator and outcome model residuals, simulate forward, and see how the estimated pathwise indirect effects change.

Usage

sensitivity_pathwise(
  model,
  exper,
  confound_ix = NULL,
  rho_seq = NULL,
  n_bootstrap = 100,
  progress = TRUE,
  ...
)

Arguments

model	A multimedia object containing the fitted models for sensitivity analysis. Note that since our approach relies on correlating simulated residual error, it is only applicable to models of class lm_model(), glmnet_model() and rf_model().
exper	The original mediation_data class object used to fit model. These observations will be resampled to support bootstrap confidence interval construction of the sensitivity curve.
confound_ix	A data.frame specifying which mediator/outcome should be allowed to be correlated. Should have two columns: 'mediator' and 'outcome' specifying which pairs of mediators and outcomes should be correlated. Defaults to NULL, which creates a data.frame with no rows (and so enforcing independence between mediators and outcomes)
rho_seq	We will evaluate correlations Cor(e', e) between mediation and outcome model errors ranging along this grid. Defaults to NULL, which internally sets the sequence to rho = (-0.9, -0.7 ..., 0.7, 0.9).
n_bootstrap	The number of bootstrap resamples used to build confidence bands around the sensitivity curves. Defaults to 100.
progress	A logical indicating whether to show a progress bar.
...	Additional arguments passed to indirect_pathwise.

Value

A date.frame giving the outputs of indirect_overall across many values of the correlation rho.

Examples

xy_data <- demo_spline()
exper <- mediation_data(
    xy_data, starts_with("outcome"), "treatment", "mediator"
)
model <- multimedia(
    exper,
    outcome_estimator = glmnet_model(lambda = 1e-2)
) |>
    estimate(exper)
rho_seq <- c(-0.2, 0, 0.2)
subset_indices <- expand.grid(
    mediator = n_mediators(model), outcome = n_outcomes(model)
)
sensitivity_pathwise(model, exper, subset_indices, rho_seq, n_bootstrap = 2)

multimedia documentation built on Sept. 30, 2024, 9:28 a.m.