DoubleMLIIVM: Double machine learning for interactive IV regression models In DoubleML: Double Machine Learning in R

 DoubleMLIIVM R Documentation

Double machine learning for interactive IV regression models

Description

Double machine learning for interactive IV regression models.

Format

R6::R6Class object inheriting from DoubleML.

Details

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)]}.

Super class

DoubleML::DoubleML -> DoubleMLIIVM

Active bindings

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.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Arguments
deep

Whether to make a deep clone.

Other DoubleML: DoubleML, DoubleMLIRM, DoubleMLPLIV, DoubleMLPLR

Examples


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()

## Not run:
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(
cp = paradox::p_dbl(lower = 0.01, upper = 0.02),
minsplit = paradox::p_int(lower = 1, upper = 2)),
cp = paradox::p_dbl(lower = 0.01, upper = 0.02),
minsplit = paradox::p_int(lower = 1, upper = 2)),
cp = paradox::p_dbl(lower = 0.01, upper = 0.02),
minsplit = paradox::p_int(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()

## End(Not run)



DoubleML documentation built on June 22, 2024, 10:50 a.m.