Double machine learning data-backend

Double machine learning data-backend.

DoubleMLData objects can be initialized from a data.table. Alternatively DoubleML provides functions to initialize from a collection of matrix objects or a data.frame. The following functions can be used to create a new instance of DoubleMLData.

• DoubleMLData$new() for initialization from a data.table.

• double_ml_data_from_matrix() for initialization from matrix objects,

• double_ml_data_from_data_frame() for initialization from a data.frame.

Active bindings

all_variables

(character())
All variables available in the dataset.

d_cols

(character())
The treatment variable(s).

data

(data.table)
Data object.

data_model

(data.table)
Internal data object that implements the causal model as specified by the user via y_col, d_cols, x_cols and z_cols.

n_instr

(NULL, integer(1))
The number of instruments.

n_obs

(integer(1))
The number of observations.

n_treat

(integer(1))
The number of treatment variables.

other_treat_cols

(NULL, character())
If use_other_treat_as_covariate is TRUE, other_treat_cols are the treatment variables that are not "active" in the multiple-treatment case. These variables then are internally added to the covariates x_cols during the fitting stage. If use_other_treat_as_covariate is FALSE, other_treat_cols is NULL.

treat_col

(character(1))
"Active" treatment variable in the multiple-treatment case.

use_other_treat_as_covariate

(logical(1))
Indicates whether in the multiple-treatment case the other treatment variables should be added as covariates. Default is TRUE.

x_cols

(NULL, character())
The covariates. If NULL, all variables (columns of data) which are neither specified as outcome variable y_col, nor as treatment variables d_cols, nor as instrumental variables z_cols are used as covariates. Default is NULL.

y_col

(character(1))
The outcome variable.

z_cols

(NULL, character())
The instrumental variables. Default is NULL.

s_col

(NULL, character())
The score or selection variable (only relevant/used for SSM Estimators). Default is NULL.

Methods

Public methods

• DoubleMLData$new()

• DoubleMLData$print()

• DoubleMLData$set_data_model()

• DoubleMLData$clone()

---

Method new()

Creates a new instance of this R6 class.

Usage

DoubleMLData$new(
  data = NULL,
  x_cols = NULL,
  y_col = NULL,
  d_cols = NULL,
  z_cols = NULL,
  s_col = NULL,
  use_other_treat_as_covariate = TRUE
)

Arguments

data

(data.table, data.frame())
Data object.

x_cols

(NULL, character())
The covariates. If NULL, all variables (columns of data) which are neither specified as outcome variable y_col, nor as treatment variables d_cols, nor as instrumental variables z_cols are used as covariates. Default is NULL.

y_col

(character(1))
The outcome variable.

d_cols

(character())
The treatment variable(s).

z_cols

(NULL, character())
The instrumental variables. Default is NULL.

s_col

(NULL, character())
The score or selection variable (only relevant/used for SSM Estimators). Default is NULL.

use_other_treat_as_covariate

(logical(1))
Indicates whether in the multiple-treatment case the other treatment variables should be added as covariates. Default is TRUE.

---

Method print()

Print DoubleMLData objects.

Usage

DoubleMLData$print()

---

Method set_data_model()

Setter function for data_model. The function implements the causal model as specified by the user via y_col, d_cols, x_cols and z_cols and assigns the role for the treatment variables in the multiple-treatment case.

Usage

DoubleMLData$set_data_model(treatment_var)

Arguments

treatment_var

(character())
Active treatment variable that will be set to treat_col.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

DoubleMLData$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(DoubleML)
df = make_plr_CCDDHNR2018(return_type = "data.table")
obj_dml_data = DoubleMLData$new(df,
  y_col = "y",
  d_cols = "d")