DoubleMLClusterData: Double machine learning data-backend for data with cluster...

In DoubleML: Double Machine Learning in R

Double machine learning data-backend for data with cluster variables

Description

Double machine learning data-backend for data with cluster variables.

DoubleMLClusterData 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 DoubleMLClusterData.

• DoubleMLClusterData$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.

Super class

DoubleML::DoubleMLData -> DoubleMLClusterData

Active bindings

cluster_cols

(character())
The cluster variable(s).

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, nor as cluster variables cluster_cols are used as covariates. Default is NULL.

n_cluster_vars

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

Methods

Public methods

• DoubleMLClusterData$new()

• DoubleMLClusterData$print()

• DoubleMLClusterData$set_data_model()

• DoubleMLClusterData$clone()

---

Method new()

Creates a new instance of this R6 class.

Usage

DoubleMLClusterData$new(
  data = NULL,
  x_cols = NULL,
  y_col = NULL,
  d_cols = NULL,
  cluster_cols = NULL,
  z_cols = 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).

cluster_cols

(character())
The cluster variable(s).

z_cols

(NULL, character())
The instrumental variables. 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 DoubleMLClusterData objects.

Usage

DoubleMLClusterData$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, z_cols and cluster_cols and assigns the role for the treatment variables in the multiple-treatment case.

Usage

DoubleMLClusterData$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

DoubleMLClusterData$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(DoubleML)
dt = make_pliv_multiway_cluster_CKMS2021(return_type = "data.table")
obj_dml_data = DoubleMLClusterData$new(dt,
  y_col = "Y",
  d_cols = "D",
  z_cols = "Z",
  cluster_cols = c("cluster_var_i", "cluster_var_j"))

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