DoubleMLPLR: Double machine learning for partially linear regression...

Description Format Details Super class Methods See Also Examples

Description

Double machine learning for partially linear regression models.

Format

R6::R6Class object inheriting from DoubleML.

Details

Partially linear regression (PLR) models take the form

Y = Dθ_0 + g_0(X) + ζ,

D = m_0(X) + V,

with E[ζ|D,X]=0 and E[V|X] = 0. Y is the outcome variable variable and D is the policy variable of interest. The high-dimensional vector X = (X_1, …, X_p) consists of other confounding covariates, and ζ and V are stochastic errors.

Super class

DoubleML::DoubleML -> DoubleMLPLR

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
DoubleMLPLR$new(
  data,
  ml_g,
  ml_m,
  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, LearnerClassif, 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"). For binary treatment variables, an object of the class LearnerClassif can be passed, for example lrn("classif.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[D|X].

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" or IV-type) or a function() specifying the score function. If a function() is provided, it must be of the form function(y, d, g_hat, m_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
DoubleMLPLR$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

Other DoubleML: DoubleMLIIVM, DoubleMLIRM, DoubleMLPLIV, DoubleML

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(data.table)
set.seed(2)
ml_g = lrn("regr.ranger", num.trees = 10, max.depth = 2)
ml_m = ml_g$clone()
obj_dml_data = make_plr_CCDDHNR2018(alpha = 0.5)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data, ml_g, ml_m)
dml_plr_obj$fit()
dml_plr_obj$summary()


## Not run: 
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(data.table)
set.seed(2)
ml_g = lrn("regr.rpart")
ml_m = ml_g$clone()
obj_dml_data = make_plr_CCDDHNR2018(alpha = 0.5)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data, ml_g, ml_m)

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))))

# minimum requirements for tune_settings
tune_settings = list(
  terminator = mlr3tuning::trm("evals", n_evals = 5),
  algorithm = mlr3tuning::tnr("grid_search", resolution = 5))
dml_plr_obj$tune(param_set = param_grid, tune_settings = tune_settings)
dml_plr_obj$fit()
dml_plr_obj$summary()

## End(Not run)

DoubleML documentation built on Oct. 26, 2021, 5:06 p.m.