Description
Inference on LATEs under Network Interference of Unknown Form.
Description
Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form. The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect. The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.
README.md
latenetwork: Inference on LATEs under Network Interference of Unknown Form
The latenetwork package provides tools for causal inference under noncompliance with treatment assignment and network interference of unknown form. The package enables to implement the instrumental variables (IV) estimation for the local average treatment effect (LATE) type parameters via inverse probability weighting (IPW) using the concept of instrumental exposure mapping (IEM) and the framework of approximate neighborhood interference (ANI). For more details, see Hoshino and Yanagi (2023) “Causal inference with noncompliance and unknown interference”.
Installation
Get the package from CRAN:
install.packages("latenetwork")
or from GitHub:
# install.packages("devtools") # if needed
devtools::install_github("tkhdyanagi/latenetwork", build_vignettes = TRUE)
Vignettes
For more details, see the package vignettes with:
library("latenetwork")
# Getting Started with the latenetwork Package
vignette("latenetwork")
# Review of Causal Inference with Noncompliance and Unknown Interference
vignette("review", package = "latenetwork")
References
- Hoshino, T. and Yanagi, T., 2023. Causal inference with noncompliance and unknown interference. arXiv preprint arXiv:2108.07455. Link.