setup_X1 {GenericML} | R Documentation |
Setup function controlling the matrix X_1
in the BLP or GATES regression
Description
Returns a list with three elements. The first element of the list, funs_Z
, controls which functions of matrix Z
are used as regressors in X_1
. The second element, covariates
, is an optional matrix of custom covariates that shall be included in X_1
. The third element, fixed_effects
, controls the inclusion of fixed effects.
Usage
setup_X1(funs_Z = c("B"), covariates = NULL, fixed_effects = NULL)
Arguments
funs_Z |
Character vector controlling the functions of |
covariates |
Optional numeric matrix containing additional covariates to be included in |
fixed_effects |
Numeric vector of integers that indicates cluster membership of the observations: For each cluster, a fixed effect will be added. Default is |
Details
The output of this setup function is intended to be used as argument in the functions GenericML()
and GenericML_single()
(arguments X1_BLP
, X1_GATES
), as well as BLP()
and GATES()
(argument X1_control
).
Value
An object of class "setup_X1"
, consisting of the following components:
funs_Z
A character vector, being a subset of
c("S", "B", "p")
.covariates
Either
NULL
or a numeric matrix.fixed_effects
Either
NULL
or an integer vector indicating cluster membership.
See the description above for details.
References
Chernozhukov V., Demirer M., Duflo E., Fernández-Val I. (2020). “Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments.” arXiv preprint arXiv:1712.04802. URL: https://arxiv.org/abs/1712.04802.
See Also
GenericML()
,
GenericML_single()
,
BLP()
,
GATES()
,
setup_vcov()
,
setup_diff()
Examples
set.seed(1)
n <- 100 # sample size
p <- 5 # number of covariates
covariates <- matrix(runif(n*p), n, p) # sample matrix of covariates
# let there be three clusters; assign membership randomly
fixed_effects <- sample(c(1,2,3), size = n, replace = TRUE)
# use BCA estimates in matrix X1
setup_X1(funs_Z = "B", covariates = NULL, fixed_effects = NULL)
# use BCA and propensity score estimates in matrix X1
# uses uniform covariates and fixed effects
setup_X1(funs_Z = c("B", "p"), covariates = covariates, fixed_effects = NULL)