DoubleML (version 0.3.0)

DoubleMLPLIV: Double machine learning for partially linear IV regression models

Description

Double machine learning for partially linear IV regression models.

Format

R6::R6Class object inheriting from DoubleML.

Super class

DoubleML::DoubleML -> DoubleMLPLIV

Active bindings

partialX

(logical(1)) Indicates whether covariates \(X\) should be partialled out.

partialZ

(logical(1)) Indicates whether instruments \(Z\) should be partialled out.

Methods

Public methods

• DoubleMLPLIV$new()

• DoubleMLPLIV$clone()

• 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()

Method new()

Creates a new instance of this R6 class.

Usage

DoubleMLPLIV$new(
  data,
  ml_g,
  ml_m,
  ml_r,
  partialX = TRUE,
  partialZ = FALSE,
  n_folds = 5,
  n_rep = 1,
  score = "partialling out",
  dml_procedure = "dml2",
  draw_sample_splitting = TRUE,
  apply_cross_fitting = TRUE
)

Arguments

data

(DoubleMLData) The DoubleMLData object providing the data and specifying the variables of the causal model.

ml_g

(LearnerRegr, character(1)) An object of the class mlr3 regression learner to pass a learner, possibly with specified parameters, for example lrn("regr.cv_glmnet", s = "lambda.min"). Alternatively, a character(1) specifying the name of a mlr3 regression learner that is available in mlr3 or its extension packages mlr3learners or mlr3extralearners, for example "regr.cv_glmnet". ml_g refers to the nuisance function \(g_0(X) = E[Y|X]\).

ml_m

(LearnerRegr, character(1)) An object of the class mlr3 regression learner to pass a learner, possibly with specified parameters, for example lrn("regr.cv_glmnet", s = "lambda.min"). Alternatively, a character(1) specifying the name of a mlr3 regression learner that is available in mlr3 or its extension packages mlr3learners or mlr3extralearners, for example "regr.cv_glmnet". ml_m refers to the nuisance function \(m_0(X) = E[Z|X]\).

ml_r

(LearnerRegr, character(1)) An object of the class mlr3 regression learner to pass a learner, possibly with specified parameters, for example lrn("regr.cv_glmnet", s = "lambda.min"). Alternatively, a character(1) specifying the name of a mlr3 regression learner that is available in mlr3 or its extension packages mlr3learners or mlr3extralearners, for example "regr.cv_glmnet". ml_r refers to the nuisance function \(r_0(X) = E[D|X]\).

partialX

(logical(1)) Indicates whether covariates \(X\) should be partialled out. Default is TRUE.

partialZ

(logical(1)) Indicates whether instruments \(Z\) should be partialled out. Default is FALSE.

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) ("partialling out" is the only choice) or a function() specifying the score function. If a function() is provided, it must be of the form function(y, z, d, g_hat, m_hat, r_hat, smpls) and the returned output must be a named list() with elements psi_a and psi_b. Default is "partialling out".

dml_procedure

(character(1)) A character(1) ("dml1" or "dml2") specifying the double machine learning algorithm. Default is "dml2".

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.

Method clone()

The objects of this class are cloneable with this method.

Usage

DoubleMLPLIV$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Partially linear IV regression (PLIV) models take the form

\(Y - D\theta_0 = g_0(X) + \zeta\),

\(Z = m_0(X) + V\),

with \(E[\zeta|Z,X]=0\) and \(E[V|X] = 0\). \(Y\) is the outcome variable variable, \(D\) is the policy variable of interest and \(Z\) denotes one or multiple instrumental variables. The high-dimensional vector \(X = (X_1, \ldots, X_p)\) consists of other confounding covariates, and \(\zeta\) and \(V\) are stochastic errors.

See Also

Other DoubleML: DoubleMLIIVM, DoubleMLIRM, DoubleMLPLR, DoubleML

Examples

# NOT RUN {
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 = ml_g$clone()
ml_r = ml_g$clone()
obj_dml_data = make_pliv_CHS2015(alpha = 1, n_obs = 500, dim_x = 20, dim_z = 1)
dml_pliv_obj = DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r)
dml_pliv_obj$fit()
dml_pliv_obj$summary()
# }
# NOT RUN {
# }
# NOT RUN {
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(data.table)
set.seed(2)
ml_g = lrn("regr.rpart")
ml_m = ml_g$clone()
ml_r = ml_g$clone()
obj_dml_data = make_pliv_CHS2015(
  alpha = 1, n_obs = 500, dim_x = 20,
  dim_z = 1)
dml_pliv_obj = DoubleMLPLIV$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_pliv_obj$tune(param_set = param_grid, tune_settings = tune_settings)
dml_pliv_obj$fit()
dml_pliv_obj$summary()
# }