Type: Package
Title: Double Machine Learning for Static Panel Models with Fixed Effects
Version: 0.1.5
Date: 2025-08-27
Maintainer: Annalivia Polselli <apolselli.econ@gmail.com>
Description: Implementation of partially linear panel regression (PLPR) models with high-dimensional confounding variables and exogenous treatment variable within the double machine learning framework. It allows the estimation of the structural parameter (treatment effect) in static panel data models with fixed effects using panel data approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. 'xtdml' is built on the object-oriented 'DoubleML' (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the 'mlr3' ecosystem.
License: GPL-2 | GPL-3
Encoding: UTF-8
Depends: R (≥ 3.5.0)
Imports: R6 (≥ 2.4.1), data.table (≥ 1.12.8), mlr3 (≥ 0.5.0), mlr3tuning (≥ 0.3.0), mlr3learners (≥ 0.3.0), mlr3misc, mvtnorm, utils, clusterGeneration, readstata13, magrittr, dplyr, stats, MLmetrics, checkmate
RoxygenNote: 7.3.2
Suggests: rpart, mlr3pipelines
NeedsCompilation: no
Packaged: 2025-09-02 16:45:19 UTC; annal
Author: Annalivia Polselli ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2025-09-08 19:00:02 UTC

Generates data from a partially linear panel regression (PLPR) model

Description

Generates data from a partially linear regression model for panel data with fixed effects similar to DGP3 (highly nonlinear) in Clarke and Polselli (2025).

The data generating process is defined as

Y_{it} = \theta D_{it} + g_0(X_{it}) + \alpha_i + U_{it},

D_{it} = m_0(X_{it}) + \gamma_i + V_i,

where U_{it} \sim \mathcal{N}(0,1), V_{it} \sim \mathcal{N}(0,1), \alpha_i = \rho A_i + \sqrt{1-\rho^2} B_i with A_i\sim \mathcal{N}(3,3) B_i\sim \mathcal{N}(0,1), and \gamma_i\sim \mathcal{N}(0,5).

The covariates are distributed as X_{it,p} \sim A_i + \mathcal{N}(0, 5), where p is the number of covariates.

The nuisance functions are given by

m_0(X_{it}) = a_1 [X_{it,1} \times 1(X_{it,1}>0)] + a_2 [X_{it,1} \times X_{it,3}],

g_0(X_{it}) = b_1 [X_{it,1} \times X_{it,3}] + b_2 [X_{it,3} \times 1(X_{it,3}>0)],

with a_1=b_2=0.25 and a_2=b_1=0.5.

Usage

make_plpr_data(n_obs = 500, t_per = 10, dim_x = 20, theta = 0.5, rho = 0.8)

Arguments

n_obs

(integer(1))
The number of cross-sectional observations (i) to simulate.

t_per

(integer(1))
The number of time periods (t) to simulate.

dim_x

(integer(1))
The number of covariates.

theta

(numeric(1))
The value of the causal parameter.

rho

(numeric(1))
Parameter governing the relationship between the covariates and the unobserved individual heterogeneity. The value is chosen between 0 (pure random effect) and 1 (pure fixed effects).

Value

A data object.

References

Clarke, P. S. and Polselli, A. (2025). Double Machine Learning for Static Panel Models with Fixed Effects. Econometrics Journal. DOI: 10.1093/ectj/utaf011.

Examples

df = make_plpr_data(n_obs = 500, t_per = 10, dim_x = 20, theta = 0.5, rho=0.8)

Abstract class xtdml

Description

Abstract base class that cannot be initialized.

Implementation of partially linear panel regression (PLPR) models with high-dimensional confounding variables and exogenous treatment variable within the double machine learning framework. It allows the estimation of the structural parameter (treatment effect) in static panel data models with fixed effects using panel data approaches established in Clarke and Polselli (2025). xtdml is built on the object-oriented DoubleML (Bach et al., 2024) using the mlr3 ecosystem.

Format

R6::R6Class object.

Active bindings

all_coef_theta

(matrix())
Estimates of the causal parameter(s) "theta" for the n_rep different sample splits after calling fit().

all_dml1_coef_theta

(array())
Estimates of the causal parameter(s) "theta" for the n_rep different sample splits after calling fit() with dml_procedure = "dml1".

all_se_theta

(matrix())
Standard errors of the causal parameter(s) "theta" for the n_rep different sample splits after calling fit().

all_model_rmse

(matrix())
Model root-mean-squared-error.

apply_cross_fitting

(logical(1))
Indicates whether cross-fitting should be applied. Default is TRUE.

coef_theta

(numeric())
Estimates for the causal parameter(s) "theta" after calling fit().

data

(data.table)
Data object.

dml_procedure

(character(1))
A character() ("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.

learner

(named list())
The machine learners for the nuisance functions.

n_folds

(integer(1))
Number of folds. Default is 5.

n_rep

(integer(1))
Number of repetitions for the sample splitting. Default is 1.

params

(named list())
The hyperparameters of the learners.

psi_theta

(array())
Value of the score function \psi(W;\theta_0,\eta_0)=-\psi_a(W;\eta_0) \theta_0 + \psi_b(W;\eta_0) after calling fit().

psi_theta_a

(array())
Value of the score function component \psi_a(W;\eta_0) after calling fit().

psi_theta_b

(array())
Value of the score function component \psi_b(W;\eta_0) after calling fit().

res_y

(array())
Residual of output equation

res_d

(array())
Residual of treatment equation

predictions

(array())
Predictions of the nuisance models after calling fit(store_predictions=TRUE).

targets

(array())
Targets of the nuisance models after calling fit(store_predictions=TRUE).

rmses

(array())
The root-mean-squared-errors of the nuisance parameters

all_model_mse

(array())
Collection of all mean-squared-errors of the model

model_rmse

(array())
The root-mean-squared-errors of the model

models

(array())
The fitted nuisance models after calling fit(store_models=TRUE).

pval_theta

(numeric())
p-values for the causal parameter(s) "theta" after calling fit().

score

(character(1))
A character(1) specifying the score function among "orth-PO", "orth-IV". Default is "orth-PO".

se_theta

(numeric())
Standard errors for the causal parameter(s) "theta" after calling fit().

smpls

(list())
The partition used for cross-fitting.

smpls_cluster

(list())
The partition used for cross-fitting. smpl is at cluster-var

t_stat_theta

(numeric())
t-statistics for the causal parameter(s) "theta" after calling fit().

tuning_res_theta

(named list())
Results from hyperparameter tuning.

Methods

Public methods

Method new()

DML with FE is an abstract class that can't be initialized.

Usage
xtdml$new()

Method print()

Print 'DML with FE' objects.

Usage
xtdml$print()

Method fit()

Estimate DML models with FE.

Usage
xtdml$fit(store_predictions = FALSE, store_models = FALSE)
Arguments
store_predictions

(logical(1))
Indicates whether the predictions for the nuisance functions should be stored in field predictions. Default is FALSE.

store_models

(logical(1))
Indicates whether the fitted models for the nuisance functions should be stored in field models if you want to analyze the models or extract information like variable importance. Default is FALSE.

Returns

self

Method split_samples()

Draw sample splitting for Double ML models with FE.

The samples are drawn according to the attributes n_folds, n_rep and apply_cross_fitting.

Usage
xtdml$split_samples()
Returns

self

Method tune()

Hyperparameter-tuning for DML models with FE.

The hyperparameter-tuning is performed using the tuning methods provided in the mlr3tuning package. For more information on tuning in mlr3, we refer to the section on parameter tuning in the mlr3 book.

Usage
xtdml$tune(
  param_set,
  tune_settings = list(n_folds_tune = 5, rsmp_tune = mlr3::rsmp("cv", folds = 5), measure
    = NULL, terminator = mlr3tuning::trm("evals", n_evals = 20), algorithm =
    mlr3tuning::tnr("grid_search"), resolution = 5),
  tune_on_folds = FALSE
)
Arguments
param_set

(named list())
A named list with a parameter grid for each nuisance model/learner (see method learner_names()). The parameter grid must be an object of class ParamSet.

tune_settings

(named list())
A named list() with arguments passed to the hyperparameter-tuning with mlr3tuning to set up TuningInstance objects. tune_settings has entries

  • terminator (Terminator)
    A Terminator object. Specification of terminator is required to perform tuning.

  • algorithm (Tuner or character(1))
    A Tuner object (recommended) or key passed to the respective dictionary to specify the tuning algorithm used in tnr(). algorithm is passed as an argument to tnr(). If algorithm is not specified by the users, default is set to "grid_search". If set to "grid_search", then additional argument "resolution" is required.

  • rsmp_tune (Resampling or character(1))
    A Resampling object (recommended) or option passed to rsmp() to initialize a Resampling for parameter tuning in mlr3. If not specified by the user, default is set to "cv" (cross-validation).

  • n_folds_tune (integer(1), optional)
    If rsmp_tune = "cv", number of folds used for cross-validation. If not specified by the user, default is set to 5.

  • measure (NULL, named list(), optional)
    Named list containing the measures used for parameter tuning. Entries in list must either be Measure objects or keys to be passed to passed to msr(). The names of the entries must match the learner names (see method learner_names()). If set to NULL, default measures are used, i.e., "regr.mse" for continuous outcome variables and "classif.ce" for binary outcomes.

  • resolution (character(1))
    The key passed to the respective dictionary to specify the tuning algorithm used in tnr(). resolution is passed as an argument to tnr().

tune_on_folds

(logical(1))
Indicates whether the tuning should be done fold-specific or globally. Default is FALSE.

Returns

self

Method summary()

Summary for DML models with FE after calling fit().

Usage
xtdml$summary(digits = max(3L, getOption("digits") - 3L))
Arguments
digits

(integer(1))
The number of significant digits to use when printing.

Method confint()

Confidence intervals for DML models with FE.

Usage
xtdml$confint(parm, joint = FALSE, level = 0.95)
Arguments
parm

(numeric() or character())
A specification of which parameters are to be given confidence intervals among the variables for which inference was done, either a vector of numbers or a vector of names. If missing, all parameters are considered (default).

joint

(logical(1))
Indicates whether joint confidence intervals are computed. Default is FALSE.

level

(numeric(1))
The confidence level. Default is 0.95.

Returns

A matrix() with the confidence interval(s).

Method learner_names()

Returns the names of the learners.

Usage
xtdml$learner_names()
Returns

character() with names of learners.

Method params_names()

Returns the names of the nuisance models with hyperparameters.

Usage
xtdml$params_names()
Returns

character() with names of nuisance models with hyperparameters.

Method set_ml_nuisance_params()

Set hyperparameters for the nuisance models of DML models with FE.

Note that in the current implementation, either all parameters have to be set globally or all parameters have to be provided fold-specific.

Usage
xtdml$set_ml_nuisance_params(
  learner = NULL,
  treat_var = NULL,
  params,
  set_fold_specific = FALSE
)
Arguments
learner

(character(1))
The nuisance model/learner (see method params_names).

treat_var

(character(1))
The treatment variAble (hyperparameters can be set treatment-variable specific).

params

(named list())
A named list() with estimator parameters for time-varying covariates. Parameters are used for all folds by default. Alternatively, parameters can be passed in a fold-specific way if option fold_specificis TRUE. In this case, the outer list needs to be of length n_rep and the inner list of length n_folds_per_cluster.

set_fold_specific

(logical(1))
Indicates if the parameters passed in params should be passed in fold-specific way. Default is FALSE. If TRUE, the outer list needs to be of length n_rep and the inner list of length n_folds_per_cluster. Note that in the current implementation, either all parameters have to be set globally or all parameters have to be provided fold-specific.

Returns

self

Method get_params()

Get hyper-parameters for the nuisance model of xtdml models.

Usage
xtdml$get_params(learner)
Arguments
learner

(character(1))
The nuisance model/learner (see method params_names())

Returns

named list()with paramers for the nuisance model/learner.

Method clone()

The objects of this class are cloneable with this method.

Usage
xtdml$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

Other xtdml: xtdml_plr

Set up for data for panel data approaches and up two cluster variables

Description

Double machine learning (DML) data-backend for data with cluster variables. xtdml_data sets up the data environment for panel data analysis with transformed variables.

xtdml_data objects can be initialized from a data.table. The following functions can be used to create a new instance of xtdml_data.

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.

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

Method 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,
  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'.

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".

Method print()

Print xtdml_data objects.

Usage
xtdml_data$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 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.

Method 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.

Wrapper for Double machine learning data-backend initialization from data.frame.

Description

Initalization of DoubleMLData from data.frame.

Usage

xtdml_data_from_data_frame(
  df,
  x_cols = NULL,
  y_col = NULL,
  d_cols = NULL,
  cluster_cols = NULL,
  approach = NULL,
  transformX = NULL
)

Arguments

df

(data.frame())
Data object.

x_cols

(character())
The covariates.

y_col

(character(1))
The outcome variable.

d_cols

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

cluster_cols

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

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".

Value

Creates a new instance of class xtdml_data.

Examples


# Generate simulated panel dataset from `xtdml`
data = make_plpr_data(n_obs = 500, t_per = 10, dim_x = 30, theta = 0.5, rho=0.8)

# Set up DML data environment
x_cols  = paste0("X", 1:30)

obj_xtdml_data = xtdml_data_from_data_frame(data,
                x_cols = x_cols,  y_col = "y", d_cols = "d",
                cluster_cols = "id", approach = "fd-exact",
                transformX = "no")

obj_xtdml_data$print()

Routine to estimate partially linear panel regression models with fixed effects within double machine learning.

Description

Routine to estimate partially linear panel regression models with fixed effects within double machine learning.

Format

R6::R6Class object inheriting from xtdml.

Details

Consider partially linear panel regression (PLR) model of form

Y_{it} = \theta_0 D_{it} + g_0(x_{it}) + \alpha_i + U_{it} (1)

D_{it} = m_0(x_{it}) + \gamma_i + V_{it} (2)

where (1) is the outcome equation and (2) is the treatment equation.

Super class

xtdml::xtdml -> xtdml_plr

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
xtdml_plr$new(
  data,
  ml_l,
  ml_m,
  ml_g = NULL,
  n_folds = 5,
  n_rep = 1,
  score = "orth-PO",
  dml_procedure = "dml2",
  draw_sample_splitting = TRUE,
  apply_cross_fitting = TRUE
)
Arguments
data

(xtdml_data)
The xtdml_data object providing the data and specifying the variables of the causal model.

ml_l

(LearnerRegr, Learner, character(1))
A learner of the class LearnerRegr, which is available from mlr3 or its extension packages mlr3learners or mlr3extralearners. Alternatively, a Learner object with public field task_type = "regr" can be passed, for example of class GraphLearner. The learner can possibly be passed with specified parameters, for example lrn("regr.cv_glmnet", s = "lambda.min").
ml_l refers to the nuisance function l_0(X) = E[Y|X].

ml_m

(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 variables, 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_m refers to the nuisance function m_0(X) = E[D|X].

ml_g

(LearnerRegr, Learner, character(1))
A learner of the class LearnerRegr, which is available from mlr3 or its extension packages mlr3learners or mlr3extralearners. Alternatively, a Learner object with public field task_type = "regr" can be passed, for example of class GraphLearner. The learner can possibly be passed with specified parameters, for example lrn("regr.cv_glmnet", s = "lambda.min").
ml_g refers to the nuisance function g_0(X) = E[Y - D\theta_0|X]. Note: The learner ml_g is only required for the score 'IV-type'. Optionally, it can be specified and estimated for callable scores.

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))
A character(1) ("orth-PO" or "orth-IV"). "orth-PO" is Neyman-orthogonal score with the partialling-out formula. "orth-IV" is Neyman-orthogonal score with the IV-type formula. Default is "orth-PO".

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 set_ml_nuisance_params()

Set hyperparameters for the nuisance models of DML models with FE.

Usage
xtdml_plr$set_ml_nuisance_params(
  learner = NULL,
  treat_var = NULL,
  params,
  set_fold_specific = FALSE
)
Arguments
learner

(character(1))
The nuisance model/learner (see method params_names).

treat_var

(character(1))
The treatment varaible (hyperparameters can be set treatment-variable specific).

params

(named list())
A named list() with estimator parameters. Parameters are used for all folds by default. Alternatively, parameters can be passed in a fold-specific way if option fold_specificis TRUE. In this case, the outer list needs to be of length n_rep and the inner list of length n_folds.

set_fold_specific

(logical(1))
Indicates if the parameters passed in params_theta should be passed in fold-specific way. Default is FALSE. If TRUE, the outer list needs to be of length n_rep and the inner list of length n_folds.

Returns

self

Method tune()

Hyperparameter-tuning within double machine learning.

The hyperparameter-tuning is performed using the tuning methods provided in the mlr3tuning package. For more information on tuning in mlr3, we refer to the section on parameter tuning in the mlr3 book.

Usage
xtdml_plr$tune(
  param_set,
  tune_settings = list(n_folds_tune = 5, rsmp_tune = mlr3::rsmp("cv", folds = 5), measure
    = NULL, terminator = mlr3tuning::trm("evals", n_evals = 20), algorithm =
    mlr3tuning::tnr("grid_search"), resolution = 5),
  tune_on_folds = FALSE
)
Arguments
param_set

(named list())
A named list with a parameter grid for each nuisance model/learner (see method learner_names()). The parameter grid must be an object of class ParamSet.

tune_settings

(named list())
A named list() with arguments passed to the hyperparameter-tuning with mlr3tuning to set up TuningInstance objects. tune_settings has entries

  • terminator (Terminator)
    A Terminator object. Specification of terminator is required to perform tuning.

  • algorithm (Tuner or character(1))
    A Tuner object (recommended) or key passed to the respective dictionary to specify the tuning algorithm used in tnr(). algorithm is passed as an argument to tnr(). If algorithm is not specified by the users, default is set to "grid_search". If set to "grid_search", then additional argument "resolution" is required.

  • rsmp_tune (Resampling or character(1))
    A Resampling object (recommended) or option passed to rsmp() to initialize a Resampling for parameter tuning in mlr3. If not specified by the user, default is set to "cv" (cross-validation).

  • n_folds_tune (integer(1), optional)
    If rsmp_tune = "cv", number of folds used for cross-validation. If not specified by the user, default is set to 5.

  • measure (NULL, named list(), optional)
    Named list containing the measures used for parameter tuning. Entries in list must either be Measure objects or keys to be passed to passed to msr(). The names of the entries must match the learner names (see method learner_names()). If set to NULL, default measures are used, i.e., "regr.mse" for continuous outcome variables and "classif.ce" for binary outcomes.

  • resolution (character(1))
    The key passed to the respective dictionary to specify the tuning algorithm used in tnr(). resolution is passed as an argument to tnr().

tune_on_folds

(logical(1))
Indicates whether the tuning should be done fold-specific or globally. Default is FALSE.

Returns

self

Examples
# Tuning example with `rpart`
library(mlr3)
library(rpart)
library(mlr3misc)
library(mlr3tuning)

# Generate simulated dataset
data = make_plpr_data(n_obs = 100, t_per = 5, dim_x = 10, theta = 0.5, rho=0.8)

x_cols  = paste0("X", 1:10)

# Set up DML data environment
obj_xtdml_data = xtdml_data_from_data_frame(data,
                x_cols = x_cols,  y_col = "y", d_cols = "d",
                cluster_cols = "id", approach = "fd-exact")

# Set up DML estimation environment
  learner = lrn("regr.rpart")
  ml_l = learner$clone()
  ml_m = learner$clone()

  obj_xtdml = xtdml_plr$new(obj_xtdml_data,
                           ml_l = ml_l, ml_m = ml_m,
                           score = "orth-PO", n_folds = 3)
# Set up a list of parameter grids
param_grid = list("ml_l" = ps(cp = p_dbl(lower = 0.01, upper = 0.02),
                            maxdepth = p_int(lower = 2, upper = 10)),
                  "ml_m" = ps(cp = p_dbl(lower = 0.01, upper = 0.02),
                            maxdepth = p_int(lower = 2, upper = 10)))

tune_settings = list(n_folds_tune = 3,
                   rsmp_tune = mlr3::rsmp("cv", folds = 3),
                   terminator = mlr3tuning::trm("evals", n_evals = 5),
                   algorithm = tnr("grid_search"), resolution = 5)

obj_xtdml$tune(param_set = param_grid, tune_settings = tune_settings)

# Estimate target/causal parameter
obj_xtdml$fit()
obj_xtdml$print()

Method clone()

The objects of this class are cloneable with this method.

Usage
xtdml_plr$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

Other xtdml: xtdml

Examples


# An illustrative example using a regression tree (`rpart`)
library(mlr3)
library(rpart)

set.seed(1234)

# Generate simulated dataset
data = make_plpr_data(n_obs = 100, t_per = 5, dim_x = 10, theta = 0.5, rho=0.8)

x_cols  = paste0("X", 1:10)

# Set up DML data environment
obj_xtdml_data = xtdml_data_from_data_frame(data,
                x_cols = x_cols,  y_col = "y", d_cols = "d",
                cluster_cols = "id", approach = "fd-exact")

# Set up DML estimation environment
 learner = lrn("regr.rpart")
 ml_l = learner$clone()
 ml_m = learner$clone()

 obj_xtdml = xtdml_plr$new(obj_xtdml_data,
                           ml_l = ml_l, ml_m = ml_m,
                           score = "orth-PO", n_folds = 3)
 obj_xtdml$fit()


## ------------------------------------------------
## Method `xtdml_plr$tune`
## ------------------------------------------------

# Tuning example with `rpart`
library(mlr3)
library(rpart)
library(mlr3misc)
library(mlr3tuning)

# Generate simulated dataset
data = make_plpr_data(n_obs = 100, t_per = 5, dim_x = 10, theta = 0.5, rho=0.8)

x_cols  = paste0("X", 1:10)

# Set up DML data environment
obj_xtdml_data = xtdml_data_from_data_frame(data,
                x_cols = x_cols,  y_col = "y", d_cols = "d",
                cluster_cols = "id", approach = "fd-exact")

# Set up DML estimation environment
  learner = lrn("regr.rpart")
  ml_l = learner$clone()
  ml_m = learner$clone()

  obj_xtdml = xtdml_plr$new(obj_xtdml_data,
                           ml_l = ml_l, ml_m = ml_m,
                           score = "orth-PO", n_folds = 3)
# Set up a list of parameter grids
param_grid = list("ml_l" = ps(cp = p_dbl(lower = 0.01, upper = 0.02),
                            maxdepth = p_int(lower = 2, upper = 10)),
                  "ml_m" = ps(cp = p_dbl(lower = 0.01, upper = 0.02),
                            maxdepth = p_int(lower = 2, upper = 10)))

tune_settings = list(n_folds_tune = 3,
                   rsmp_tune = mlr3::rsmp("cv", folds = 3),
                   terminator = mlr3tuning::trm("evals", n_evals = 5),
                   algorithm = tnr("grid_search"), resolution = 5)

obj_xtdml$tune(param_set = param_grid, tune_settings = tune_settings)

# Estimate target/causal parameter
obj_xtdml$fit()
obj_xtdml$print()