xtdml_data: Data-backed DML Environment

In xtdml: Double Machine Learning for Static Panel Models with Fixed Effects

Data-backed DML Environment

Description

Data-backed environment for Double machine learning (DML) that cannot be initialized. xtdml_data sets up the data environment for panel data analysis with transformed variables. The xtdml_data_from_data_frame() function can be used to create a new instance of xtdml_data from a data.frame.

Active bindings

all_variables

(character())
All variables available in the data frame.

d_cols

(character())
The treatment variable.

dbar_col

(NULL, character()')
The individual mean of the treatment variable.

data

(data.table)
Data object.

data_model

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

n_obs

(integer(1))
The number of observations.

n_treat

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

treat_col

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

x_cols

(character())
The covariates.

y_col

(character(1))
The outcome variable.

panel_id

(character())
The panel identifier.

time_id

(character())
The time identifier.

cluster_cols

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

n_cluster_vars

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

approach

(character(1))
A character() ("fd-exact", "wg-approx" or "cre") specifying the panel data technique to apply to estimate the causal model. Default is "fd-exact".

transformX

(character(1))
A character() ("no", "minmax" or "poly") specifying the type of transformation to apply to the X data. "no" does not transform the covariates X and is recommended for tree-based learners. "minmax" applies the Min-Max normalization x' = (x-x_{min})/(x_{max}-x_{min}) to the covariates and is recommended with neural networks. "poly" add polynomials up to order three and interactions between all possible combinations of two and three variables; this is recommended for Lasso. Default is "no".

Methods

Public methods

• xtdml_data$new()

• xtdml_data$print()

• xtdml_data$plot()

• xtdml_data$set_data_model()

• xtdml_data$clone()

---

xtdml_data$new()

Creates a new instance of this R6 class.

Usage

xtdml_data$new(
  data = NULL,
  x_cols = NULL,
  y_col = NULL,
  d_cols = NULL,
  dbar_col = NULL,
  panel_id = NULL,
  time_id = NULL,
  cluster_cols = NULL,
  approach = NULL,
  transformX = NULL
)

Arguments

data

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

x_cols

(character())

y_col

(character(1))
The outcome variable.

d_cols

(character(1))
The treatment variable.

dbar_col

(NULL, character()⁠) \cr Individual mean of the treatment variable (used for the CRE approach). Default is ⁠NULL'.

panel_id

(character())
The panel identifier.

time_id

(character())
The time identifier.

cluster_cols

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

approach

(character(1))
A character() ("fd-exact", "wg-approx" or "cre") specifying the panel data technique to apply to estimate the causal model. Default is "fd-exact".

transformX

(character(1))
A character() ("no", "minmax" or "poly") specifying the type of transformation to apply to the X data. "no" does not transform the covariates X and is recommended for tree-based learners. "minmax" applies the Min-Max normalization x' = (x-x_{min})/(x_{max}-x_{min}) to the covariates and is recommended with neural networks. "poly" add polynomials up to order three and interactions between all possible combinations of two and three variables; this is recommended for Lasso. Default is "no".

---

xtdml_data$print()

Print xtdml_data objects.

Usage

xtdml_data$print()

---

xtdml_data$plot()

Plotting method, which is not implemented for xtdml objects.

Attempting to call it returns an informative message. Use the print() method to view xtdml_data objects.

Usage

xtdml_data$plot()

---

xtdml_data$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, panel_id, time_id and cluster_cols and assigns the role for the treatment variables in the multiple-treatment case.

Usage

xtdml_data$set_data_model(treatment_var)

Arguments

treatment_var

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

---

xtdml_data$clone()

The objects of this class are cloneable with this method.

Usage

xtdml_data$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

xtdml documentation built on Sept. 2, 2026, 1:07 a.m.