Causal Inference using Multivariate Generalized Propensity Score

Methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) . The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.1 by Justin Williams, 3 days ago

Report a bug at

Browse source code at

Authors: Justin Williams [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS

Suggests testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2

See at CRAN