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Inferring Causal Effects on Collective Outcomes under Interference

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About

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) .

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Version 0.2.0
Published 2020-02-16 1691 days ago
Needs compilation? yes
License GPL (≥ 3)
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Maintainer

Maintainer

Youjin Lee

Authors

Elizabeth Ogburn

aut

Ilya Shpitser

aut

Youjin Lee

aut / cre

Material

README
Reference manual
Package source

In Views

CausalInference

Vignettes

Estimation of probability associated with collective counterfactual outcomes

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Old Sources

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Imports

Rcpp ≥ 0.12.17
Matrix
gtools
stringr
stats
igraph

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knitr
rmarkdown
testthat
R.rsp

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Rcpp