R/double_ml_iivm.R
DoubleMLIIVM.RdDouble 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()
}