R/double_ml_iivm.R
DoubleMLIIVM.Rd
Double machine learning for interactive IV regression models.
R6::R6Class object inheriting from DoubleML.
Interactive IV regression (IIVM) models take the form
\(Y = \ell_0(D,X) + \zeta\),
\(Z = m_0(X) + V\),
with \(E[\zeta|X,Z]=0\) and \(E[V|X] = 0\). \(Y\) is the outcome variable, \(D \in \{0,1\}\) is the binary treatment variable and \(Z \in \{0,1\}\) is a binary instrumental variable. Consider the functions \(g_0\), \(r_0\) and \(m_0\), where \(g_0\) maps the support of \((Z,X)\) to \(R\) and \(r_0\) and \(m_0\), respectively, map the support of \((Z,X)\) and \(X\) to \((\epsilon, 1-\epsilon)\) for some \(\epsilon \in (1, 1/2)\), such that
\(Y = g_0(Z,X) + \nu,\)
\(D = r_0(Z,X) + U,\)
\(Z = m_0(X) + V,\)
with \(E[\nu|Z,X]=0\), \(E[U|Z,X]=0\) and \(E[V|X]=0\). The target parameter of interest in this model is the local average treatment effect (LATE),
\(\theta_0 = \frac{E[g_0(1,X)] - E[g_0(0,X)]}{E[r_0(1,X)] - E[r_0(0,X)]}.\)
Other DoubleML:
DoubleMLIRM
,
DoubleMLPLIV
,
DoubleMLPLR
,
DoubleML
DoubleML::DoubleML
-> DoubleMLIIVM
subgroups
(named list(2)
)
Named list(2)
with options to adapt to cases with and without the
subgroups of always-takers and never-takes.
The entry always_takers
(logical(1)
) speficies whether there are
always takers in the sample. The entry never_takers
(logical(1)
)
speficies whether there are never takers in the sample.
trimming_rule
(character(1)
)
A character(1)
specifying the trimming approach.
trimming_threshold
(numeric(1)
)
The threshold used for timming.
Inherited methods
DoubleML::DoubleML$bootstrap()
DoubleML::DoubleML$confint()
DoubleML::DoubleML$fit()
DoubleML::DoubleML$get_params()
DoubleML::DoubleML$learner_names()
DoubleML::DoubleML$p_adjust()
DoubleML::DoubleML$params_names()
DoubleML::DoubleML$print()
DoubleML::DoubleML$set_ml_nuisance_params()
DoubleML::DoubleML$set_sample_splitting()
DoubleML::DoubleML$split_samples()
DoubleML::DoubleML$summary()
DoubleML::DoubleML$tune()
new()
Creates a new instance of this R6 class.
DoubleMLIIVM$new(
data,
ml_g,
ml_m,
ml_r,
n_folds = 5,
n_rep = 1,
score = "LATE",
subgroups = list(always_takers = TRUE, never_takers = TRUE),
dml_procedure = "dml2",
trimming_rule = "truncate",
trimming_threshold = 1e-12,
draw_sample_splitting = TRUE,
apply_cross_fitting = TRUE
)
data
(DoubleMLData
)
The DoubleMLData
object providing the data and specifying the variables
of the causal model.
ml_g
(LearnerRegr
,
LearnerClassif
, Learner
,
character(1)
)
A learner of the class LearnerRegr
, which is
available from mlr3 or its
extension packages mlr3learners or
mlr3extralearners.
For binary treatment outcomes, an object of the class
LearnerClassif
can be passed, for example
lrn("classif.cv_glmnet", s = "lambda.min")
.
Alternatively, a Learner
object with public field
task_type = "regr"
or task_type = "classif"
can be passed,
respectively, for example of class
GraphLearner
. ml_g
refers to the nuisance function \(g_0(Z,X) = E[Y|X,Z]\).
ml_m
(LearnerClassif
,
Learner
, character(1)
)
A learner of the class LearnerClassif
, which is
available from mlr3 or its
extension packages mlr3learners or
mlr3extralearners.
Alternatively, a Learner
object with public field
task_type = "classif"
can be passed, for example of class
GraphLearner
. The learner can possibly
be passed with specified parameters, for example
lrn("classif.cv_glmnet", s = "lambda.min")
. ml_m
refers to the nuisance function \(m_0(X) = E[Z|X]\).
ml_r
(LearnerClassif
,
Learner
, character(1)
)
A learner of the class LearnerClassif
, which is
available from mlr3 or its
extension packages mlr3learners or
mlr3extralearners.
Alternatively, a Learner
object with public field
task_type = "classif"
can be passed, for example of class
GraphLearner
. The learner can possibly
be passed with specified parameters, for example
lrn("classif.cv_glmnet", s = "lambda.min")
. ml_r
refers to the nuisance function \(r_0(Z,X) = E[D|X,Z]\).
n_folds
(integer(1)
)
Number of folds. Default is 5
.
n_rep
(integer(1)
)
Number of repetitions for the sample splitting. Default is 1
.
score
(character(1)
, function()
)
A character(1)
("LATE"
is the only choice) specifying the score
function.
If a function()
is provided, it must be of the form
function(y, z, d, g0_hat, g1_hat, m_hat, r0_hat, r1_hat, smpls)
and
the returned output must be a named list()
with elements psi_a
and
psi_b
. Default is "LATE"
.
subgroups
(named list(2)
)
Named list(2)
with options to adapt to cases with and without the
subgroups of always-takers and never-takes. The entry
always_takers
(logical(1)
) speficies whether there are always takers
in the sample. The entry never_takers
(logical(1)
) speficies whether
there are never takers in the sample. Default is
list(always_takers = TRUE, never_takers = TRUE)
.
dml_procedure
(character(1)
)
A character(1)
("dml1"
or "dml2"
) specifying the double machine
learning algorithm. Default is "dml2"
.
trimming_rule
(character(1)
)
A character(1)
("truncate"
is the only choice) specifying the
trimming approach. Default is "truncate"
.
trimming_threshold
(numeric(1)
)
The threshold used for timming. Default is 1e-12
.
draw_sample_splitting
(logical(1)
)
Indicates whether the sample splitting should be drawn during
initialization of the object. Default is TRUE
.
apply_cross_fitting
(logical(1)
)
Indicates whether cross-fitting should be applied. Default is TRUE
.
# \donttest{
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(data.table)
set.seed(2)
ml_g = lrn("regr.ranger",
num.trees = 100, mtry = 20,
min.node.size = 2, max.depth = 5)
ml_m = lrn("classif.ranger",
num.trees = 100, mtry = 20,
min.node.size = 2, max.depth = 5)
ml_r = ml_m$clone()
obj_dml_data = make_iivm_data(
theta = 0.5, n_obs = 1000,
alpha_x = 1, dim_x = 20)
dml_iivm_obj = DoubleMLIIVM$new(obj_dml_data, ml_g, ml_m, ml_r)
dml_iivm_obj$fit()
dml_iivm_obj$summary()
#> Estimates and significance testing of the effect of target variables
#> Estimate. Std. Error t value Pr(>|t|)
#> d 0.5418 0.2149 2.522 0.0117 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#>
# }
if (FALSE) {
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(data.table)
set.seed(2)
ml_g = lrn("regr.rpart")
ml_m = lrn("classif.rpart")
ml_r = ml_m$clone()
obj_dml_data = make_iivm_data(
theta = 0.5, n_obs = 1000,
alpha_x = 1, dim_x = 20)
dml_iivm_obj = DoubleMLIIVM$new(obj_dml_data, ml_g, ml_m, ml_r)
param_grid = list(
"ml_g" = paradox::ParamSet$new(list(
paradox::ParamDbl$new("cp", lower = 0.01, upper = 0.02),
paradox::ParamInt$new("minsplit", lower = 1, upper = 2))),
"ml_m" = paradox::ParamSet$new(list(
paradox::ParamDbl$new("cp", lower = 0.01, upper = 0.02),
paradox::ParamInt$new("minsplit", lower = 1, upper = 2))),
"ml_r" = paradox::ParamSet$new(list(
paradox::ParamDbl$new("cp", lower = 0.01, upper = 0.02),
paradox::ParamInt$new("minsplit", lower = 1, upper = 2))))
# minimum requirements for tune_settings
tune_settings = list(
terminator = mlr3tuning::trm("evals", n_evals = 5),
algorithm = mlr3tuning::tnr("grid_search", resolution = 5))
dml_iivm_obj$tune(param_set = param_grid, tune_settings = tune_settings)
dml_iivm_obj$fit()
dml_iivm_obj$summary()
}