mvGPS: 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) <arXiv:2008.13767>. 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.
| Version: |
1.2.2 |
| Depends: |
R (≥ 3.6) |
| Imports: |
Rdpack, MASS, WeightIt, cobalt, matrixNormal, geometry, sp, gbm, CBPS |
| Suggests: |
testthat, knitr, dagitty, ggdag, dplyr, rmarkdown, ggplot2 |
| Published: |
2021-12-07 |
| Author: |
Justin Williams 
[aut, cre] |
| Maintainer: |
Justin Williams <williazo at ucla.edu> |
| BugReports: |
https://github.com/williazo/mvGPS/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/williazo/mvGPS |
| NeedsCompilation: |
no |
| Citation: |
mvGPS citation info |
| Materials: |
NEWS |
| In views: |
CausalInference |
| CRAN checks: |
mvGPS results |
Documentation:
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