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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

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Version

Install

install.packages('latenetwork')

Monthly Downloads

155

Version

1.0.1

License

MIT + file LICENSE

Maintainer

Takahide Yanagi

Last Published

August 8th, 2023

Functions in latenetwork (1.0.1)

datageneration

Generate Artificial Data by Simulation
indirect

Inference on Average Indirect Effect Parameters
direct

Inference on Average Direct Effect Parameters
overall

Inference on Average Overall Effect Parameters
spillover

Inference on Average Spillover Effect Parameters