dot-compute_gps_weights: Compute stabilised IPW weights from a GPS model (continuous...

In RobustMediate: Causal Mediation Analysis with Diagnostics and Sensitivity Analysis

Compute stabilised IPW weights from a GPS model (continuous treatment)

Description

Implements the stabilised weight formula from Robins, Hernán & Brumback (2000) for continuous exposures:

w_i = \frac{f(X_i \mid \bar{X}, \hat{\sigma}^2_m)}{f(X_i \mid Z_i, \hat{\sigma}^2_c)}

where the numerator is the marginal density of X and the denominator is the conditional density given covariates. Both are evaluated as Gaussian PDFs using the model residual standard deviation.

Weights are trimmed at the 1st and 99th percentile to reduce the influence of extreme propensity scores (positivity violations).

Usage

.compute_gps_weights(gps_model, data, treat_var, trim = c(0.01, 0.99))

Arguments

gps_model	Fitted GPS model from .fit_gps_model().
data	Original data frame.
treat_var	Name of the treatment variable (character).
trim	Quantile trimming bounds. Default c(0.01, 0.99).

Value

Numeric vector of stabilised, trimmed IPW weights (length = nrow(data)).

References

Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.

Hirano, K. & Imbens, G. W. (2004). The propensity score with continuous treatments. In A. Gelman & X.-L. Meng (Eds.), Applied Bayesian modeling and causal inference from incomplete-data perspectives (pp. 73–84). Wiley.

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