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) <doi:10.48550/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 |
| DOI: | 10.32614/CRAN.package.mvGPS |
| Author: | Justin Williams [aut, cre] |
| Maintainer: | Justin Williams <williazo at ucla.edu> |
| BugReports: | https://github.com/williazo/mvGPS/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/williazo/mvGPS |
| NeedsCompilation: | no |
| Citation: | mvGPS citation info |
| Materials: | NEWS |
| In views: | CausalInference |
| CRAN checks: | mvGPS results |
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