Getting Started with the latenetwork Package

Introduction

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).

The parameters of interest are as follows.

For more details on the identification and estimation methods, see the “Review of Causal Inference with Noncompliance and Unknown Interference” vignette with: vignette("review", package = "latenetwork").

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)

Functions

The latenetwork package provides the following functions:

Arguments

All package functions have the following arguments:

The direct() function has the following additional arguments:

The spillover() function has the following additional arguments:

Returns

Each function returns a data.frame with the following elements:

Example

To run the following example, install the igraph package if needed.

# if needed --------------------------------------------------------------------
install.packages("igraph")

Generate artificial data from the datageneration() function.

# Generate artificial data from a ring network----------------------------------
set.seed(1)
n <- 2000
data <- latenetwork::datageneration(n = n)

Perform the causal inference with:

# Arguments --------------------------------------------------------------------
Y   <- data$Y
D   <- data$D
Z   <- data$Z
A   <- data$A
IEM <- ifelse(A %*% Z > 0, 1, 0)
S   <- rep(TRUE, n)
K   <- 1
z   <- 1
t   <- 0
t0  <- 0
t1  <- 1
bw  <- NULL
B   <- NULL
alp <- 0.05

# Causal inference -------------------------------------------------------------

# The ADE parameters defined by IEM = (A %*% Z > 0)
result_direct1 <- latenetwork::direct(Y = Y,
                                      D = D,
                                      Z = Z,
                                      IEM = IEM,
                                      S = S,
                                      A = A,
                                      K = K,
                                      t = t,
                                      bw = bw,
                                      B = B,
                                      alp = alp)

# The ADE parameters defined by the constant IEM
result_direct2 <- latenetwork::direct(Y = Y,
                                      D = D,
                                      Z = Z,
                                      IEM = NULL,
                                      S = S,
                                      A = A,
                                      K = K,
                                      t = NULL,
                                      bw = bw,
                                      B = B,
                                      alp = alp)

# The AIE parameters defined by K = 1
result_indirect <- latenetwork::indirect(Y = Y,
                                         D = D,
                                         Z = Z,
                                         S = S,
                                         A = A,
                                         K = K,
                                         bw = bw,
                                         B = B,
                                         alp = alp)

# The AOE parameters defined by K = 1
result_overall <- latenetwork::overall(Y = Y,
                                       D = D,
                                       Z = Z,
                                       S = S,
                                       A = A,
                                       K = K,
                                       bw = bw,
                                       B = B,
                                       alp = alp)

# The ASE parameters defined by IEM = (A %*% Z > 0)
result_spillover <- latenetwork::spillover(Y = Y,
                                           D = D,
                                           Z = Z,
                                           IEM = IEM,
                                           S = S,
                                           A = A,
                                           K = K,
                                           z = z,
                                           t0 = t0,
                                           t1 = t1,
                                           bw = bw,
                                           B = B,
                                           alp = alp)

You can see the estimation results with:

result_direct1
#>            est     HAC_SE  HAC_CI_L  HAC_CI_U wild_SE wild_CI_L wild_CI_U bw
#> ADEY 0.4008916 0.09871458 0.2074146 0.5943686      NA        NA        NA  8
#> ADED 0.2499606 0.03485485 0.1816464 0.3182749      NA        NA        NA  8
#> LADE 1.6038190 0.36023112 0.8977789 2.3098590      NA        NA        NA  8
#>      size
#> ADEY 2000
#> ADED 2000
#> LADE 2000

result_direct2
#>            est     HAC_SE  HAC_CI_L  HAC_CI_U wild_SE wild_CI_L wild_CI_U bw
#> ADEY 0.5632636 0.05254325 0.4602807 0.6662465      NA        NA        NA  8
#> ADED 0.3551812 0.02213500 0.3117974 0.3985650      NA        NA        NA  8
#> LADE 1.5858485 0.12418001 1.3424602 1.8292368      NA        NA        NA  8
#>      size
#> ADEY 2000
#> ADED 2000
#> LADE 2000

result_indirect
#>            est     HAC_SE  HAC_CI_L  HAC_CI_U wild_SE wild_CI_L wild_CI_U bw
#> AIEY 0.2924892 0.08785062 0.1203051 0.4646732      NA        NA        NA  8
#> AIED 0.2897227 0.03205981 0.2268866 0.3525587      NA        NA        NA  8
#> ADED 0.3551812 0.02213500 0.3117974 0.3985650      NA        NA        NA  8
#> LAIE 0.8234928 0.25796895 0.3178830 1.3291027      NA        NA        NA  8
#>      size
#> AIEY 2000
#> AIED 2000
#> ADED 2000
#> LAIE 2000

result_overall
#>            est     HAC_SE  HAC_CI_L  HAC_CI_U wild_SE wild_CI_L wild_CI_U bw
#> AOEY 0.8557528 0.09429867 0.6709308 1.0405748      NA        NA        NA  8
#> AOED 0.6449039 0.03744014 0.5715226 0.7182852      NA        NA        NA  8
#> ADED 0.3551812 0.02213500 0.3117974 0.3985650      NA        NA        NA  8
#> LAOE 2.4093413 0.27637076 1.8676646 2.9510181      NA        NA        NA  8
#>      size
#> AOEY 2000
#> AOED 2000
#> ADED 2000
#> LAOE 2000

result_spillover
#>            est     HAC_SE  HAC_CI_L  HAC_CI_U wild_SE wild_CI_L wild_CI_U bw
#> ASEY 0.5750447 0.08065202 0.4169696 0.7331197      NA        NA        NA  8
#> ASED 0.3920457 0.03401795 0.3253718 0.4587197      NA        NA        NA  8
#> LASE 1.4667795 0.18557907 1.1030512 1.8305078      NA        NA        NA  8
#>      size
#> ASEY 2000
#> ASED 2000
#> LASE 2000

References

Hoshino, T. and Yanagi, T., 2023. Causal inference with noncompliance and unknown interference. arXiv preprint arXiv:2108.07455. Link

Leung, M.P. (2022). Causal inference under approximate neighborhood interference. Econometrica, 90(1), pp.267-293. Link

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