direct {latenetwork} | R Documentation |
Inference on Average Direct Effect Parameters
Description
Inference on the average direct effect of the IV on the outcome, that on the treatment receipt, and the local average direct effect in the presence of network spillover of unknown form
Usage
direct(
Y,
D,
Z,
IEM = NULL,
S,
A,
K = 1,
t = NULL,
bw = NULL,
B = NULL,
alp = 0.05
)
Arguments
Y |
An n-dimensional outcome vector |
D |
An n-dimensional binary treatment vector |
Z |
An n-dimensional binary instrumental vector |
IEM |
An n-dimensional instrumental exposure vector.
If |
S |
An n-dimensional logical vector to indicate whether each unit belongs to the sub-population S |
A |
An n times n symmetric binary adjacency matrix |
K |
A scalar to indicate the range of neighborhood
used for constructing the interference set.
Default is 1.
In the |
t |
A scalar of the evaluation point of IEM. Default is NULL. |
bw |
A scalar of the bandwidth used for the HAC estimation and
the wild bootstrap.
If |
B |
The number of bootstrap repetitions.
If |
alp |
The significance level. Default is 0.05. |
Details
The direct()
function estimates the average direct effect of the IV
on the outcome, that on the treatment receipt, and
the local average direct effect via inverse probability weighting
in the approximate neighborhood interference framework.
The function also computes the standard errors and the confidence intervals
for the target parameters based on the network HAC estimation and
the wild bootstrap.
For more details, see Hoshino and Yanagi (2023).
The lengths of Y
, D
, Z
, S
and
of the row and column of A
must be the same.
IEM
must be NULL
or a vector of the same length as Y
.
t
must be NULL
or a value in the support of IEM
.
K
must be a positive integer.
bw
must be NULL
or a non-negative integer.
B
must be NULL
or a positive number.
alp
must be a positive number between 0 and 0.5.
Value
A data.frame containing the following elements:
est |
The parameter estimate |
HAC_SE |
The standard error computed by the network HAC estimation |
HAC_CI_L |
The lower bound of the confidence interval computed by the network HAC estimation |
HAC_CI_U |
The upper bound of the confidence interval computed by the network HAC estimation |
wild_SE |
The standard error computed by the wild bootstrap |
wild_CI_L |
The lower bound of the confidence interval computed by the wild bootstrap |
wild_CI_U |
The upper bound of the confidence interval computed by the wild bootstrap |
bw |
The bandwidth used for the HAC estimation and the wild bootstrap |
size |
The size of the subpopulation S |
References
Hoshino, T., & Yanagi, T. (2023). Causal inference with noncompliance and unknown interference. arXiv preprint arXiv:2108.07455.
Leung, M.P. (2022). Causal inference under approximate neighborhood interference. Econometrica, 90(1), pp.267-293.
Examples
# Generate artificial data
set.seed(1)
n <- 2000
data <- latenetwork::datageneration(n = n)
# Arguments
Y <- data$Y
D <- data$D
Z <- data$Z
IEM <- data$IEM
S <- rep(TRUE, n)
A <- data$A
K <- 1
t <- 0
bw <- NULL
B <- NULL
alp <- 0.05
# Estimation
latenetwork::direct(Y = Y,
D = D,
Z = Z,
IEM = IEM,
S = S,
A = A,
K = K,
t = t,
bw = bw,
B = B,
alp = alp)