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Double machine learning for interactive regression models

Source: R/double_ml_irm.R
DoubleMLIRM.Rd

Double machine learning for interactive regression models.

Format

R6::R6Class object inheriting from DoubleML.

Details

Interactive regression (IRM) models take the form

\(Y = g_0(D,X) + U\),

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

with \(E[U|X,D]=0\) and \(E[V|X] = 0\). \(Y\) is the outcome variable and \(D \in \{0,1\}\) is the binary treatment variable. We consider estimation of the average treamtent effects when treatment effects are fully heterogeneous. Target parameters of interest in this model are the average treatment effect (ATE),

\(\theta_0 = E[g_0(1,X) - g_0(0,X)]\)

and the average treament effect on the treated (ATTE),

\(\theta_0 = E[g_0(1,X) - g_0(0,X)|D=1]\).

See also

Other DoubleML: DoubleML, DoubleMLIIVM, DoubleMLPLIV, DoubleMLPLR, DoubleMLSSM

Super class

DoubleML::DoubleML -> DoubleMLIRM

Active bindings

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.

Usage

DoubleMLIRM$new(
  data,
  ml_g,
  ml_m,
  n_folds = 5,
  n_rep = 1,
  score = "ATE",
  trimming_rule = "truncate",
  trimming_threshold = 1e-12,
  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, 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 \(g_0(X) = E[Y|X,D]\).

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 \(m_0(X) = E[D|X]\).

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) ("ATE" or ATTE) or a function() specifying the score function. If a function() is provided, it must be of the form function(y, d, g0_hat, g1_hat, m_hat, smpls) and the returned output must be a named list() with elements psi_a and psi_b. Default is "ATE".

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.

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

DoubleMLIRM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \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)
obj_dml_data = make_irm_data(theta = 0.5)
dml_irm_obj = DoubleMLIRM$new(obj_dml_data, ml_g, ml_m)
dml_irm_obj$fit()
dml_irm_obj$summary()
#> Estimates and significance testing of the effect of target variables
#>   Estimate. Std. Error t value Pr(>|t|)  
#> d    0.6658     0.2866   2.323   0.0202 *
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> 
# }
if (FALSE) { # \dontrun{
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(data.table)
set.seed(2)
ml_g = lrn("regr.rpart")
ml_m = lrn("classif.rpart")
obj_dml_data = make_irm_data(theta = 0.5)
dml_irm_obj = DoubleMLIRM$new(obj_dml_data, ml_g, ml_m)

param_grid = list(
  "ml_g" = paradox::ps(
    cp = paradox::p_dbl(lower = 0.01, upper = 0.02),
    minsplit = paradox::p_int(lower = 1, upper = 2)),
  "ml_m" = paradox::ps(
    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_irm_obj$tune(param_set = param_grid, tune_settings = tune_settings)
dml_irm_obj$fit()
dml_irm_obj$summary()
} # }