In networks, treatments may spill over from the treated individual to his or her social contacts and outcomes may be contagious over time. Under this setting, causal inference on the collective outcome observed over all network is often of interest. We use chain graph models approximating the projection of the full longitudinal data onto the observed data to identify the causal effect of the intervention on the whole outcome. Justification of such approximation is demonstrated in Ogburn et al. (2018) <arXiv:1812.04990>.
| Version: | 0.2.0 |
| Imports: | Rcpp (≥ 0.12.17), Matrix, gtools, stringr, stats, igraph |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, testthat, R.rsp |
| Published: | 2020-02-16 |
| Author: | Elizabeth Ogburn [aut], Ilya Shpitser [aut], Youjin Lee [aut, cre] |
| Maintainer: | Youjin Lee <youjin.lee at pennmedicine.upenn.edu> |
| License: | GPL (≥ 3) | file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | netchain results |
| Reference manual: | netchain.pdf |
| Vignettes: |
Estimation of probability associated with collective counterfactual outcomes |
| Package source: | netchain_0.2.0.tar.gz |
| Windows binaries: | r-devel: netchain_0.2.0.zip, r-release: netchain_0.2.0.zip, r-oldrel: netchain_0.2.0.zip |
| macOS binaries: | r-release: netchain_0.2.0.tgz, r-oldrel: netchain_0.2.0.tgz |
| Old sources: | netchain archive |
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