Title: Tidy Estimation of Heterogeneous Treatment Effects
Version: 1.0.4
Description: Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.
URL: https://github.com/ddimmery/tidyhte https://ddimmery.github.io/tidyhte/index.html
BugReports: https://github.com/ddimmery/tidyhte/issues
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: covr, devtools, estimatr, ggplot2, glmnet, knitr, mockr, nprobust, palmerpenguins, quadprog, quickblock, rmarkdown, testthat (≥ 3.0.0), vimp, WeightedROC
Config/testthat/edition: 3
Imports: checkmate, dplyr, lifecycle, magrittr, progress, purrr, R6, rlang, SuperLearner, tibble
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-07-29 13:55:01 UTC; drewd
Author: Drew Dimmery ORCID iD [aut, cre, cph]
Maintainer: Drew Dimmery <cran@ddimmery.com>
Repository: CRAN
Date/Publication: 2025-07-29 19:20:02 UTC

tidyhte: Tidy Estimation of Heterogeneous Treatment Effects

Description

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) doi:10.1214/23-EJS2157. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.

Details

The best place to get started with tidyhte is vignette("experimental_analysis") which walks through a full analysis of HTE on simulated data, or vignette("methodological_details") which gets into more of the details underlying the method.

Author(s)

Maintainer: Drew Dimmery cran@ddimmery.com (ORCID) [copyright holder]

References

Kennedy, E. H. (2020). Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497.

See Also

The core public-facing functions are make_splits, produce_plugin_estimates, construct_pseudo_outcomes and estimate_QoI. Configuration is accomplished through HTE_cfg in addition to a variety of related classes (see basic_config).

Configuration of a Constant Estimator

Description

Constant_cfg is a configuration class for estimating a constant model. That is, the model is a simple, one-parameter mean model.

Super class

tidyhte::Model_cfg -> Constant_cfg

Public fields

model_class

The class of the model, required for all classes which inherit from Model_cfg.

Methods

Public methods

Method new()

Create a new Constant_cfg object.

Usage
Constant_cfg$new()
Returns

A new Constant_cfg object.

Examples
Constant_cfg$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
Constant_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `Constant_cfg$new`
## ------------------------------------------------

Constant_cfg$new()

Configuration of Model Diagnostics

Description

Diagnostics_cfg is a configuration class for estimating a variety of diagnostics for the models trained in the course of HTE estimation.

Public fields

ps

Model diagnostics for the propensity score model.

outcome

Model diagnostics for the outcome models.

effect

Model diagnostics for the joint effect model.

params

Parameters for any requested diagnostics.

Methods

Public methods

Method new()

Create a new Diagnostics_cfg object with specified diagnostics to estimate.

Usage
Diagnostics_cfg$new(ps = NULL, outcome = NULL, effect = NULL, params = NULL)
Arguments
ps

Model diagnostics for the propensity score model.

outcome

Model diagnostics for the outcome models.

effect

Model diagnostics for the joint effect model.

params

List providing values for parameters to any requested diagnostics.

Returns

A new Diagnostics_cfg object.

Examples
Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE", "RROC"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)

Method add()

Add diagnostics to the Diagnostics_cfg object.

Usage
Diagnostics_cfg$add(ps = NULL, outcome = NULL, effect = NULL)
Arguments
ps

Model diagnostics for the propensity score model.

outcome

Model diagnostics for the outcome models.

effect

Model diagnostics for the joint effect model.

Returns

An updated Diagnostics_cfg object.

Examples
cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE", "RROC"),
   ps = c("SL_risk", "SL_coefs")
)
cfg <- cfg$add(ps = "AUC")

Method clone()

The objects of this class are cloneable with this method.

Usage
Diagnostics_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE", "RROC"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)

## ------------------------------------------------
## Method `Diagnostics_cfg$new`
## ------------------------------------------------

Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE", "RROC"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)

## ------------------------------------------------
## Method `Diagnostics_cfg$add`
## ------------------------------------------------

cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE", "RROC"),
   ps = c("SL_risk", "SL_coefs")
)
cfg <- cfg$add(ps = "AUC")

Predictor class for the cross-fit predictor of "partial" CATEs

Description

Predictor class for the cross-fit predictor of "partial" CATEs

Predictor class for the cross-fit predictor of "partial" CATEs

Details

The class makes it easier to manage the K predictors for retrieving K-fold cross-validated estimates, as well as to measure how treatment effects change when only a single covariate is changed from its "natural" levels (in the sense "natural" used by the direct / indirect effects literature).

Public fields

models

A list of the K model fits

num_splits

The number of folds used in cross-fitting.

num_mc_samples

The number of samples to retrieve across the covariate space. If num_mc_samples is larger than the sample size, then the entire dataset will be used.

covariates

The unquoted names of the covariates used in the second-stage model.

model_class

The model class (in the sense of Model_cfg). For instance, a SuperLearner model will have model class "SL".

Methods

Public methods

Method new()

FX.predictor is a class which simplifies the management of a set of cross-fit prediction models of treatment effects and provides the ability to get the "partial" effects of particular covariates.

Usage
FX.Predictor$new(models, num_splits, num_mc_samples, covariates, model_class)
Arguments
models

A list of the K model fits.

num_splits

Integer number of cross-fitting folds.

num_mc_samples

Integer number of Monte-Carlo samples across the covariate space. If this is larger than the sample size, then the whole dataset will be used.

covariates

The unquoted names of the covariates.

model_class

The model class (in the sense of Model_cfg).

Method predict()

Predicts the PCATE surface over a particular covariate, returning a tibble with the predicted HTE for every Monte-Carlo sample.

Usage
FX.Predictor$predict(data, covariate)
Arguments
data

The full dataset

covariate

The unquoted covariate name for which to calculate predicted treatment effects.

Returns

A tibble with columns:

Method clone()

The objects of this class are cloneable with this method.

Usage
FX.Predictor$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

R6 class to represent partitions of the data between training and held-out

Description

R6 class to represent partitions of the data between training and held-out

R6 class to represent partitions of the data between training and held-out

Details

This takes a set of folds calculated elsewhere and represents these folds in a consistent format.

Public fields

train

A dataframe containing only the training set

holdout

A dataframe containing only the held-out data

in_holdout

A logical vector indicating if the initial data lies in the holdout set.

Methods

Public methods

Method new()

Creates an R6 object of the data split between training and test set.

Usage
HTEFold$new(data, split_id)
Arguments
data

The dataset to be split

split_id

An identifier indicating which data should lie in the holdout set.

Returns

Returns an object of class HTEFold

Method clone()

The objects of this class are cloneable with this method.

Usage
HTEFold$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Configuration of Quantities of Interest

Description

HTE_cfg is a configuration class that pulls everything together, indicating the full configuration for a given HTE analysis. This includes how to estimate models and what Quantities of Interest to calculate based off those underlying models.

Public fields

outcome

Model_cfg object indicating how outcome models should be estimated.

treatment

Model_cfg object indicating how the propensity score model should be estimated.

effect

Model_cfg object indicating how the joint effect model should be estimated.

qoi

QoI_cfg object indicating what the Quantities of Interest are and providing all necessary detail on how they should be estimated.

verbose

Logical indicating whether to print debugging information.

Methods

Public methods

Method new()

Create a new HTE_cfg object with all necessary information about how to carry out an HTE analysis.

Usage
HTE_cfg$new(
  outcome = NULL,
  treatment = NULL,
  effect = NULL,
  qoi = NULL,
  verbose = FALSE
)
Arguments
outcome

Model_cfg object indicating how outcome models should be estimated.

treatment

Model_cfg object indicating how the propensity score model should be estimated.

effect

Model_cfg object indicating how the joint effect model should be estimated.

qoi

QoI_cfg object indicating what the Quantities of Interest are and providing all necessary detail on how they should be estimated.

verbose

Logical indicating whether to print debugging information.

Examples
mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)
qoi_cfg <- QoI_cfg$new(
    mcate = mcate_cfg,
    pcate = pcate_cfg,
    vimp = vimp_cfg,
    diag = diag_cfg
)
ps_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
y_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
fx_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
HTE_cfg$new(outcome = y_cfg, treatment = ps_cfg, effect = fx_cfg, qoi = qoi_cfg)

Method clone()

The objects of this class are cloneable with this method.

Usage
HTE_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `HTE_cfg$new`
## ------------------------------------------------

mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)
qoi_cfg <- QoI_cfg$new(
    mcate = mcate_cfg,
    pcate = pcate_cfg,
    vimp = vimp_cfg,
    diag = diag_cfg
)
ps_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
y_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
fx_cfg <- SLEnsemble_cfg$new(
   learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)
HTE_cfg$new(outcome = y_cfg, treatment = ps_cfg, effect = fx_cfg, qoi = qoi_cfg)

Configuration for a Kernel Smoother

Description

KernelSmooth_cfg is a configuration class for non-parametric local-linear regression to construct a smooth representation of the relationship between two variables. This is typically used for displaying a surface of the conditional average treatment effect over a continuous covariate.

Kernel smoothing is handled by the nprobust package.

Super class

tidyhte::Model_cfg -> KernelSmooth_cfg

Public fields

model_class

The class of the model, required for all classes which inherit from Model_cfg.

neval

The number of points at which to evaluate the local regression. More points will provide a smoother line at the cost of somewhat higher computation.

eval_min_quantile

Minimum quantile at which to evaluate the smoother.

Methods

Public methods

Method new()

Create a new KernelSmooth_cfg object with specified number of evaluation points.

Usage
KernelSmooth_cfg$new(neval = 100, eval_min_quantile = 0.05)
Arguments
neval

The number of points at which to evaluate the local regression. More points will provide a smoother line at the cost of somewhat higher computation.

eval_min_quantile

Minimum quantile at which to evaluate the smoother. A value of zero will do no clipping. Clipping is performed from both the top and the bottom of the empirical distribution. A value of alpha would evaluate over [alpha, 1 - alpha].

Returns

A new KernelSmooth_cfg object.

Examples
KernelSmooth_cfg$new(neval = 100)

Method clone()

The objects of this class are cloneable with this method.

Usage
KernelSmooth_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

nprobust::lprobust

Examples


## ------------------------------------------------
## Method `KernelSmooth_cfg$new`
## ------------------------------------------------

KernelSmooth_cfg$new(neval = 100)

Configuration of Known Model

Description

Known_cfg is a configuration class for when a particular model is known a-priori. The prototypical usage of this class is when heterogeneous treatment effects are estimated in the context of a randomized control trial with known propensity scores.

Super class

tidyhte::Model_cfg -> Known_cfg

Public fields

covariate_name

The name of the column in the dataset which corresponds to the known model score.

model_class

The class of the model, required for all classes which inherit from Model_cfg.

Methods

Public methods

Method new()

Create a new Known_cfg object with specified covariate column.

Usage
Known_cfg$new(covariate_name)
Arguments
covariate_name

The name of the column, a string, in the dataset corresponding to the known model score (i.e. the true conditional expectation).

Returns

A new Known_cfg object.

Examples
Known_cfg$new("propensity_score")

Method clone()

The objects of this class are cloneable with this method.

Usage
Known_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `Known_cfg$new`
## ------------------------------------------------

Known_cfg$new("propensity_score")

Configuration of Marginal CATEs

Description

MCATE_cfg is a configuration class for estimating marginal response surfaces based on heterogeneous treatment effect estimates. "Marginal" in this context implies that all other covariates are marginalized. Thus, if two covariates are highly correlated, it is likely that their MCATE surfaces will be extremely similar.

Public fields

cfgs

Named list of covariates names to a Model_cfg object defining how to present that covariate's CATE surface (while marginalizing over all other covariates).

std_errors

Boolean indicating whether the results should be returned with standard errors or not.

estimand

String indicating the estimand to target.

Methods

Public methods

Method new()

Create a new MCATE_cfg object with specified model name and hyperparameters.

Usage
MCATE_cfg$new(cfgs, std_errors = TRUE)
Arguments
cfgs

Named list from moderator name to a Model_cfg object defining how to present that covariate's CATE surface (while marginalizing over all other covariates)

std_errors

Boolean indicating whether the results should be returned with standard errors or not.

Returns

A new MCATE_cfg object.

Examples
MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))

Method add_moderator()

Add a moderator to the MCATE_cfg object. This entails defining a configuration for displaying the effect surface for that moderator.

Usage
MCATE_cfg$add_moderator(var_name, cfg)
Arguments
var_name

The name of the moderator to add (and the name of the column in the dataset).

cfg

A Model_cfg defining how to display the selected moderator's effect surface.

Returns

An updated MCATE_cfg object.

Examples
cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
cfg <- cfg$add_moderator("x2", KernelSmooth_cfg$new(neval = 100))

Method clone()

The objects of this class are cloneable with this method.

Usage
MCATE_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))

## ------------------------------------------------
## Method `MCATE_cfg$new`
## ------------------------------------------------

MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))

## ------------------------------------------------
## Method `MCATE_cfg$add_moderator`
## ------------------------------------------------

cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
cfg <- cfg$add_moderator("x2", KernelSmooth_cfg$new(neval = 100))

Base Class of Model Configurations

Description

Model_cfg is the base class from which all other model configurations inherit.

Public fields

model_class

The class of the model, required for all classes which inherit from Model_cfg.

Methods

Public methods

Method new()

Create a new Model_cfg object with any necessary parameters.

Usage
Model_cfg$new()
Returns

A new Model_cfg object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Model_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

R6 class to represent data to be used in estimating a model

Description

R6 class to represent data to be used in estimating a model

R6 class to represent data to be used in estimating a model

Details

This class provides consistent names and interfaces to data which will be used in a supervised regression / classification model.

Public fields

label

The labels for the eventual model as a vector.

features

The matrix representation of the data to be used for model fitting. Constructed using stats::model.matrix.

model_frame

The data-frame representation of the data as constructed by stats::model.frame.

split_id

The split identifiers as a vector.

num_splits

The integer number of splits in the data.

cluster

A cluster ID as a vector, constructed using the unit identifiers.

weights

The case-weights as a vector.

Methods

Public methods

Method new()

Creates an R6 object to represent data to be used in a prediction model.

Usage
Model_data$new(data, label_col, ..., .weight_col = NULL)
Arguments
data

The full dataset to populate the class with.

label_col

The unquoted name of the column to use as the label in supervised learning models.

...

The unquoted names of features to use in the model.

.weight_col

The unquoted name of the column to use as case-weights in subsequent models.

Returns

A Model_data object.

Examples
library("dplyr")
df <- dplyr::tibble(
    uid = 1:100,
    x1 = rnorm(100),
    x2 = rnorm(100),
    x3 = sample(4, 100, replace = TRUE)
) %>% dplyr::mutate(
    y = x1 + x2 + x3 + rnorm(100),
    x3 = factor(x3)
)
df <- make_splits(df, uid, .num_splits = 5)
data <- Model_data$new(df, y, x1, x2, x3)

Method SL_cv_control()

A helper function to create the cross-validation options to be used by SuperLearner.

Usage
Model_data$SL_cv_control()

Method clone()

The objects of this class are cloneable with this method.

Usage
Model_data$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

SuperLearner::SuperLearner.CV.control

Examples


## ------------------------------------------------
## Method `Model_data$new`
## ------------------------------------------------

library("dplyr")
df <- dplyr::tibble(
    uid = 1:100,
    x1 = rnorm(100),
    x2 = rnorm(100),
    x3 = sample(4, 100, replace = TRUE)
) %>% dplyr::mutate(
    y = x1 + x2 + x3 + rnorm(100),
    x3 = factor(x3)
)
df <- make_splits(df, uid, .num_splits = 5)
data <- Model_data$new(df, y, x1, x2, x3)

Configuration of Partial CATEs

Description

[Experimental] PCATE_cfg is a configuration class for estimating marginal response surfaces based on heterogeneous treatment effect estimates. "Partial" in this context is used similarly to the use in partial dependence plots or in partial regression. In essence, a PCATE attempts to partial out the contribution to the CATE from all other covariates. Two highly correlated variables may have very different PCATE surfaces.

Public fields

cfgs

Named list of covariates names to a Model_cfg object defining how to present that covariate's CATE surface.

model_covariates

A character vector of all the covariates to be included in the second-level effect regression.

num_mc_samples

A named list from covariate name to the number of Monte Carlo samples to take to calculate the double integral (See Details).

estimand

String indicating the estimand to target.

Methods

Public methods

Method new()

Create a new PCATE_cfg object with specified model name and hyperparameters.

Usage
PCATE_cfg$new(model_covariates, cfgs, num_mc_samples = 100)
Arguments
model_covariates

A character vector of all the covariates to be included in the second-level effect regression.

cfgs

Named list from moderator name to a Model_cfg object defining how to present that covariate's CATE surface.

num_mc_samples

A named list from covariate name to the number of Monte Carlo samples to take to calculate the double integral (See Details). If all covariates should use the same number of samples, simply pass the (integer) number of samples.

effect_cfg

A Model_cfg object indicating how to fit the second level effect regression (joint across all selected covariates).

Returns

A new PCATE_cfg object.

Examples
PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)

Method add_moderator()

Add a moderator to the PCATE_cfg object. This entails adding it to the joint model of effects and defines a configuration for displaying the effect surface for that moderator.

Usage
PCATE_cfg$add_moderator(var_name, cfg)
Arguments
var_name

The name of the moderator to add (and the name of the column in the dataset).

cfg

A Model_cfg defining how to display the selected moderator's effect surface.

Returns

An updated PCATE_cfg object.

Examples
cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
cfg <- cfg$add_moderator("x2", KernelSmooth_cfg$new(neval = 100))

Method clone()

The objects of this class are cloneable with this method.

Usage
PCATE_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)

## ------------------------------------------------
## Method `PCATE_cfg$new`
## ------------------------------------------------

PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)

## ------------------------------------------------
## Method `PCATE_cfg$add_moderator`
## ------------------------------------------------

cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
cfg <- cfg$add_moderator("x2", KernelSmooth_cfg$new(neval = 100))

Configuration of Quantities of Interest

Description

QoI_cfg is a configuration class for the Quantities of Interest to be generated by the HTE analysis.

Public fields

mcate

A configuration object of type MCATE_cfg of marginal effects to calculate.

pcate

A configuration object of type PCATE_cfg of partial effects to calculate.

vimp

A configuration object of type VIMP_cfg of variable importance to calculate.

diag

A configuration object of type Diagnostics_cfg of model diagnostics to calculate.

ate

Logical flag indicating whether an estimate of the ATE should be returned.

predictions

Logical flag indicating whether estimates of the CATE for every unit should be returned.

Methods

Public methods

Method new()

Create a new QoI_cfg object with specified Quantities of Interest to estimate.

Usage
QoI_cfg$new(
  mcate = NULL,
  pcate = NULL,
  vimp = NULL,
  diag = NULL,
  ate = TRUE,
  predictions = FALSE
)
Arguments
mcate

A configuration object of type MCATE_cfg of marginal effects to calculate.

pcate

A configuration object of type PCATE_cfg of partial effects to calculate.

vimp

A configuration object of type VIMP_cfg of variable importance to calculate.

diag

A configuration object of type Diagnostics_cfg of model diagnostics to calculate.

ate

A logical flag for whether to calculate the Average Treatment Effect (ATE) or not.

predictions

A logical flag for whether to return predictions of the CATE for every unit or not.

Returns

A new Diagnostics_cfg object.

Examples
mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
    mcate = mcate_cfg,
    pcate = pcate_cfg,
    vimp = vimp_cfg,
    diag = diag_cfg
)

Method clone()

The objects of this class are cloneable with this method.

Usage
QoI_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
    mcate = mcate_cfg,
    pcate = pcate_cfg,
    vimp = vimp_cfg,
    diag = diag_cfg
)

## ------------------------------------------------
## Method `QoI_cfg$new`
## ------------------------------------------------

mcate_cfg <- MCATE_cfg$new(cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)))
pcate_cfg <- PCATE_cfg$new(
   cfgs = list(x1 = KernelSmooth_cfg$new(neval = 100)),
   model_covariates = c("x1", "x2", "x3"),
   num_mc_samples = list(x1 = 100)
)
vimp_cfg <- VIMP_cfg$new()
diag_cfg <- Diagnostics_cfg$new(
   outcome = c("SL_risk", "SL_coefs", "MSE"),
   ps = c("SL_risk", "SL_coefs", "AUC")
)
QoI_cfg$new(
    mcate = mcate_cfg,
    pcate = pcate_cfg,
    vimp = vimp_cfg,
    diag = diag_cfg
)

Elastic net regression with pairwise interactions

Description

Penalized regression using elastic net. Alpha = 0 corresponds to ridge regression and alpha = 1 corresponds to Lasso. Included in the model are pairwise interactions between covariates.

See vignette("glmnet_beta", package = "glmnet") for a nice tutorial on glmnet.

Usage

SL.glmnet.interaction(
  Y,
  X,
  newX,
  family,
  obsWeights,
  id,
  alpha = 1,
  nfolds = 10,
  nlambda = 100,
  useMin = TRUE,
  loss = "deviance",
  ...
)

Arguments

Y

Outcome variable

X

Covariate dataframe

newX

Dataframe to predict the outcome

family

"gaussian" for regression, "binomial" for binary classification. Untested options: "multinomial" for multiple classification or "mgaussian" for multiple response, "poisson" for non-negative outcome with proportional mean and variance, "cox".

obsWeights

Optional observation-level weights

id

Optional id to group observations from the same unit (not used currently).

alpha

Elastic net mixing parameter, range [0, 1]. 0 = ridge regression and 1 = lasso.

nfolds

Number of folds for internal cross-validation to optimize lambda.

nlambda

Number of lambda values to check, recommended to be 100 or more.

useMin

If TRUE use lambda that minimizes risk, otherwise use 1 standard-error rule which chooses a higher penalty with performance within one standard error of the minimum (see Breiman et al. 1984 on CART for background).

loss

Loss function, can be "deviance", "mse", or "mae". If family = binomial can also be "auc" or "class" (misclassification error).

...

Any additional arguments are passed through to cv.glmnet.

Configuration for a SuperLearner Ensemble

Description

SLEnsemble_cfg is a configuration class for estimation of a model using an ensemble of models using SuperLearner.

Super class

tidyhte::Model_cfg -> SLEnsemble_cfg

Public fields

cvControl

A list of parameters for controlling the cross-validation used in SuperLearner.

SL.library

A vector of the names of learners to include in the SuperLearner ensemble.

SL.env

An environment containing all of the programmatically generated learners to be included in the SuperLearner ensemble.

family

stats::family object to determine how SuperLearner should be fitted.

model_class

The class of the model, required for all classes which inherit from Model_cfg.

Methods

Public methods

Method new()

Create a new SLEnsemble_cfg object with specified settings.

Usage
SLEnsemble_cfg$new(
  cvControl = NULL,
  learner_cfgs = NULL,
  family = stats::gaussian()
)
Arguments
cvControl

A list of parameters for controlling the cross-validation used in SuperLearner. For more details, see SuperLearner::SuperLearner.CV.control.

learner_cfgs

A list of SLLearner_cfg objects.

family

stats::family object to determine how SuperLearner should be fitted.

Returns

A new SLEnsemble_cfg object.

Examples
SLEnsemble_cfg$new(
learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)

Method add_sublearner()

Adds a model (or class of models) to the SuperLearner ensemble. If hyperparameter values are specified, this method will add a learner for every element in the cross-product of provided hyperparameter values.

Usage
SLEnsemble_cfg$add_sublearner(learner_name, hps = NULL)
Arguments
learner_name

Possible values use SuperLearner naming conventions. A full list is available with SuperLearner::listWrappers("SL")

hps

A named list of hyper-parameters. Every element of the cross-product of these hyper-parameters will be included in the ensemble. cfg <- SLEnsemble_cfg$new( learner_cfgs = list(SLLearner_cfg$new("SL.glm")) ) cfg <- cfg$add_sublearner("SL.gam", list(deg.gam = c(2, 3)))

Method clone()

The objects of this class are cloneable with this method.

Usage
SLEnsemble_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

SLEnsemble_cfg$new(
learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)

## ------------------------------------------------
## Method `SLEnsemble_cfg$new`
## ------------------------------------------------

SLEnsemble_cfg$new(
learner_cfgs = list(SLLearner_cfg$new("SL.glm"), SLLearner_cfg$new("SL.gam"))
)

Configuration of SuperLearner Submodel

Description

SLLearner_cfg is a configuration class for a single sublearner to be included in SuperLearner. By constructing with a named list of hyperparameters, this configuration allows distinct submodels for each unique combination of hyperparameters. To understand what models and hyperparameters are available, examine the methods listed in SuperLearner::listWrappers("SL").

Public fields

model_name

The name of the model as passed to SuperLearner through the SL.library parameter.

hyperparameters

Named list from hyperparameter name to a vector of values that should be swept over.

Methods

Public methods

Method new()

Create a new SLLearner_cfg object with specified model name and hyperparameters.

Usage
SLLearner_cfg$new(model_name, hp = NULL)
Arguments
model_name

The name of the model as passed to SuperLearner through the SL.library parameter.

hp

Named list from hyperparameter name to a vector of values that should be swept over. Hyperparameters not included in this list are left at their SuperLearner default values.

Returns

A new SLLearner_cfg object.

Examples
SLLearner_cfg$new("SL.glm")
SLLearner_cfg$new("SL.gam", list(deg.gam = c(2, 3)))

Method clone()

The objects of this class are cloneable with this method.

Usage
SLLearner_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `SLLearner_cfg$new`
## ------------------------------------------------

SLLearner_cfg$new("SL.glm")
SLLearner_cfg$new("SL.gam", list(deg.gam = c(2, 3)))

Configuration for a Stratification Estimator

Description

Stratified_cfg is a configuration class for stratifying a covariate and calculating statistics within each cell.

Super class

tidyhte::Model_cfg -> Stratified_cfg

Public fields

model_class

The class of the model, required for all classes which inherit from Model_cfg.

covariate

The name of the column in the dataset which corresponds to the covariate on which to stratify.

Methods

Public methods

Method new()

Create a new Stratified_cfg object with specified number of evaluation points.

Usage
Stratified_cfg$new(covariate)
Arguments
covariate

The name of the column in the dataset which corresponds to the covariate on which to stratify.

Returns

A new Stratified_cfg object.

Examples
Stratified_cfg$new(covariate = "test_covariate")

Method clone()

The objects of this class are cloneable with this method.

Usage
Stratified_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `Stratified_cfg$new`
## ------------------------------------------------

Stratified_cfg$new(covariate = "test_covariate")

Configuration of Variable Importance

Description

VIMP_cfg is a configuration class for estimating a variable importance measure across all moderators. This provides a meaningful measure of which moderators explain the most of the CATE surface.

Public fields

estimand

String indicating the estimand to target.

sample_splitting

Logical indicating whether to use sample splitting in the calculation of variable importance.

linear

Logical indicating whether the variable importance assuming a linear model should be estimated.

Methods

Public methods

Method new()

Create a new VIMP_cfg object with specified model configuration.

Usage
VIMP_cfg$new(sample_splitting = TRUE, linear_only = FALSE)
Arguments
sample_splitting

Logical indicating whether to use sample splitting in the calculation of variable importance. Choosing not to use sample splitting means that inference will only be valid for moderators with non-null importance.

linear_only

Logical indicating whether the variable importance should use only a single linear-only model. Variable importance measure will only be consistent for the population quantity if the true model of pseudo-outcomes is linear.

Returns

A new VIMP_cfg object.

Examples
VIMP_cfg$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
VIMP_cfg$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Examples

VIMP_cfg$new()

## ------------------------------------------------
## Method `VIMP_cfg$new`
## ------------------------------------------------

VIMP_cfg$new()

Add an additional diagnostic to the effect model

Description

This adds a diagnostic to the effect model.

Usage

add_effect_diagnostic(hte_cfg, diag)

Arguments

hte_cfg

HTE_cfg object to update.

diag

Character indicating the name of the diagnostic to include. Possible values are "MSE", "RROC" and, for SuperLearner ensembles, "SL_risk" and "SL_coefs".

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_effect_diagnostic("RROC") -> hte_cfg

Add an additional model to the joint effect ensemble

Description

This adds a learner to the ensemble used for estimating a model of the conditional expectation of the pseudo-outcome.

Usage

add_effect_model(hte_cfg, model_name, ...)

Arguments

hte_cfg

HTE_cfg object to update.

model_name

Character indicating the name of the model to incorporate into the joint effect ensemble. Possible values use SuperLearner naming conventions. A full list is available with SuperLearner::listWrappers("SL")

...

Parameters over which to grid-search for this model class.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_effect_model("SL.glm.interaction") -> hte_cfg

Uses a known propensity score

Description

This replaces the propensity score model with a known value of the propensity score.

Usage

add_known_propensity_score(hte_cfg, covariate_name)

Arguments

hte_cfg

HTE_cfg object to update.

covariate_name

Character indicating the name of the covariate name in the dataframe corresponding to the known propensity score.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_known_propensity_score("ps") -> hte_cfg

Adds moderators to the configuration

Description

This adds a definition about how to display a moderators to the MCATE config. A moderator is any variable that you want to view information about CATEs with respect to.

Usage

add_moderator(hte_cfg, model_type, ..., .model_arguments = NULL)

Arguments

hte_cfg

HTE_cfg object to update.

model_type

Character indicating the model type for these moderators. Currently two model types are supported: "Stratified" for discrete moderators and "KernelSmooth" for continuous ones.

...

The (unquoted) names of the moderator variables.

.model_arguments

A named list from argument name to value to pass into the constructor for the model. See Stratified_cfg and KernelSmooth_cfg for more details.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_moderator("Stratified", x2, x3) %>%
   add_moderator("KernelSmooth", x1, x4, x5) -> hte_cfg

Add an additional diagnostic to the outcome model

Description

This adds a diagnostic to the outcome model.

Usage

add_outcome_diagnostic(hte_cfg, diag)

Arguments

hte_cfg

HTE_cfg object to update.

diag

Character indicating the name of the diagnostic to include. Possible values are "MSE", "RROC" and, for SuperLearner ensembles, "SL_risk" and "SL_coefs".

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_outcome_diagnostic("RROC") -> hte_cfg

Add an additional model to the outcome ensemble

Description

This adds a learner to the ensemble used for estimating a model of the conditional expectation of the outcome.

Usage

add_outcome_model(hte_cfg, model_name, ...)

Arguments

hte_cfg

HTE_cfg object to update.

model_name

Character indicating the name of the model to incorporate into the outcome ensemble. Possible values use SuperLearner naming conventions. A full list is available with SuperLearner::listWrappers("SL")

...

Parameters over which to grid-search for this model class.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_outcome_model("SL.glm.interaction") -> hte_cfg

Add an additional diagnostic to the propensity score

Description

This adds a diagnostic to the propensity score.

Usage

add_propensity_diagnostic(hte_cfg, diag)

Arguments

hte_cfg

HTE_cfg object to update.

diag

Character indicating the name of the diagnostic to include. Possible values are "MSE", "AUC" and, for SuperLearner ensembles, "SL_risk" and "SL_coefs".

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_propensity_diagnostic(c("AUC", "MSE")) -> hte_cfg

Add an additional model to the propensity score ensemble

Description

This adds a learner to the ensemble used for estimating propensity scores.

Usage

add_propensity_score_model(hte_cfg, model_name, ...)

Arguments

hte_cfg

HTE_cfg object to update.

model_name

Character indicating the name of the model to incorporate into the propensity score ensemble. Possible values use SuperLearner naming conventions. A full list is available with SuperLearner::listWrappers("SL")

...

Parameters over which to grid-search for this model class.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   add_propensity_score_model("SL.glmnet", alpha = c(0, 0.5, 1)) -> hte_cfg

Adds variable importance information

Description

This adds a variable importance quantity of interest to the outputs.

Usage

add_vimp(hte_cfg, sample_splitting = TRUE, linear_only = FALSE)

Arguments

hte_cfg

HTE_cfg object to update.

sample_splitting

Logical indicating whether to use sample splitting or not. Choosing not to use sample splitting means that inference will only be valid for moderators with non-null importance.

linear_only

Logical indicating whether the variable importance should use only a single linear-only model. Variable importance measure will only be consistent for the population quantity if the true model of pseudo-outcomes is linear.

Value

Updated HTE_cfg object

References

Examples

library("dplyr")
basic_config() %>%
   add_vimp(sample_splitting = FALSE) -> hte_cfg

Attach an HTE_cfg to a dataframe

Description

This adds a configuration attribute to a dataframe for HTE estimation. This configuration details the full analysis of HTE that should be performed.

Usage

attach_config(data, .HTE_cfg)

Arguments

data

dataframe

.HTE_cfg

HTE_cfg object representing the full configuration of the HTE analysis.

Details

For information about how to set up an HTE_cfg object, see the Recipe API documentation basic_config().

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

See Also

basic_config(), make_splits(), produce_plugin_estimates(), construct_pseudo_outcomes(), estimate_QoI()

Examples

library("dplyr")
if(require("palmerpenguins")) {
data(package = 'palmerpenguins')
penguins$unitid = seq_len(nrow(penguins))
penguins$propensity = rep(0.5, nrow(penguins))
penguins$treatment = rbinom(nrow(penguins), 1, penguins$propensity)
cfg <- basic_config() %>% 
add_known_propensity_score("propensity") %>%
add_outcome_model("SL.glm.interaction") %>%
remove_vimp()
attach_config(penguins, cfg) %>%
make_splits(unitid, .num_splits = 4) %>%
produce_plugin_estimates(outcome = body_mass_g, treatment = treatment, species, sex) %>%
construct_pseudo_outcomes(body_mass_g, treatment) %>%
estimate_QoI(species, sex)
}

Create a basic config for HTE estimation

Description

This provides a basic recipe for HTE estimation that can be extended by providing additional information about models to be estimated and what quantities of interest should be returned based on those models. This basic model includes only linear models for nuisance function estimation, and basic diagnostics.

Usage

basic_config()

Details

Additional models, diagnostics and quantities of interest should be added using their respective helper functions provided as part of the Recipe API.

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

Value

HTE_cfg object

See Also

add_propensity_score_model(), add_known_propensity_score(), add_propensity_diagnostic(), add_outcome_model(), add_outcome_diagnostic(), add_effect_model(), add_effect_diagnostic(), add_moderator(), add_vimp()

Examples

library("dplyr")
basic_config() %>%
   add_known_propensity_score("ps") %>%
   add_outcome_model("SL.glm.interaction") %>%
   add_outcome_model("SL.glmnet", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
   add_outcome_model("SL.glmnet.interaction", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
   add_outcome_diagnostic("RROC") %>%
   add_effect_model("SL.glm.interaction") %>%
   add_effect_model("SL.glmnet", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
   add_effect_model("SL.glmnet.interaction", alpha = c(0.05, 0.15, 0.2, 0.25, 0.5, 0.75)) %>%
   add_effect_diagnostic("RROC") %>%
   add_moderator("Stratified", x2, x3) %>%
   add_moderator("KernelSmooth", x1, x4, x5) %>%
   add_vimp(sample_splitting = FALSE) -> hte_cfg

Calculates a SATE and a PATE using AIPW

Description

This function takes fully prepared data (with all auxilliary columns from the necessary models) and estimates average treatment effects using AIPW.

Usage

calculate_ate(data)

Arguments

data

The dataset of interest after it has been prepared fully.

References

See Also

basic_config(), attach_config(), make_splits(), produce_plugin_estimates(), construct_pseudo_outcomes(), estimate_QoI()

Calculate diagnostics

Description

This function calculates the diagnostics requested by the Diagnostics_cfg object.

Usage

calculate_diagnostics(data, treatment, outcome, .diag.cfg)

Arguments

data

Data frame with all additional columns (such as model predictions) included.

treatment

Unquoted treatment variable name

outcome

Unquoted outcome variable name

.diag.cfg

Diagnostics_cfg object

Value

Returns a tibble with columns:

See Also

Diagnostics_cfg

Calculate Linear Variable Importance of HTEs

Description

calculate_linear_vimp estimates the linear hypothesis test of removing a particular moderator from a linear model containing all moderators. Unlike calculate_vimp, this will only be unbiased and have correct asymptotic coverage rates if the true model is linear. This linear approach is also substantially faster, so may be useful when prototyping an analysis.

Usage

calculate_linear_vimp(
  full_data,
  weight_col,
  pseudo_outcome,
  ...,
  .VIMP_cfg,
  .Model_cfg
)

Arguments

full_data

dataframe

weight_col

Unquoted name of the weight column.

pseudo_outcome

Unquoted name of the pseudo-outcome.

...

Unquoted names of covariates to include in the joint effect model. The variable importance will be calculated for each of these covariates.

.VIMP_cfg

A VIMP_cfg object defining how VIMP should be estimated.

.Model_cfg

A Model_cfg object defining how the joint effect model should be estimated.

References

See Also

calculate_vimp()

Calculate "partial" CATE estimates

Description

[Experimental]

Usage

calculate_pcate_quantities(
  full_data,
  .weights,
  .outcome,
  fx_model,
  ...,
  .MCATE_cfg
)

Regression ROC Curve calculation

Description

This function calculates the RegressionROC Curve of of Hernández-Orallo doi:10.1016/j.patcog.2013.06.014. It provides estimates for the positive and negative errors when predictions are shifted by a variety of constants (which range across the domain of observed residuals). Curves closer to the axes are, in general, to be preferred. In general, this curve provides a simple way to visualize the error properties of a regression model.

Usage

calculate_rroc(label, prediction, nbins = 100)

Arguments

label

True label

prediction

Model prediction of the label (out of sample)

nbins

Number of shift values to sweep over

Details

The dot shows the errors when no shift is applied, corresponding to the base model predictions.

Value

A tibble with nbins rows.

References

Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition, 46(12), 3395-3411.

Calculate Variable Importance of HTEs

Description

calculate_vimp estimates the reduction in (population) $R^2$ from removing a particular moderator from a model containing all moderators.

Usage

calculate_vimp(
  full_data,
  weight_col,
  pseudo_outcome,
  ...,
  .VIMP_cfg,
  .Model_cfg
)

Arguments

full_data

dataframe

weight_col

Unquoted name of the weight column.

pseudo_outcome

Unquoted name of the pseudo-outcome.

...

Unquoted names of covariates to include in the joint effect model. The variable importance will be calculated for each of these covariates.

.VIMP_cfg

A VIMP_cfg object defining how VIMP should be estimated.

.Model_cfg

A Model_cfg object defining how the joint effect model should be estimated.

References

See Also

calculate_linear_vimp()

Checks that a dataframe has an attached configuration for HTEs

Description

This helper function ensures that the provided dataframe has the necessary auxilliary configuration information for HTE estimation.

Usage

check_data_has_hte_cfg(data)

Arguments

data

Dataframe of interest.

Value

Returns NULL. Errors if a problem is discovered.

Checks that an appropriate identifier has been provided

Description

This helper function makes a few simple checks to identify obvious issues with the way provided column of unit identifiers.

Usage

check_identifier(data, id_col)

Arguments

data

Dataframe of interest.

id_col

Quoted name of identifier column.

Value

Returns NULL. Errors if a problem is discovered.

Checks that nuisance models have been estimated and exist in the supplied dataset.

Description

This helper function makes a few simple checks to identify obvious issues with the way that nuisance functions are created and prepared.

Usage

check_nuisance_models(data)

Arguments

data

Dataframe which should have appropriate columns of nuisance function predictions: .pi_hat, .mu0_hat, and .mu1_hat

Value

Returns NULL. Errors if a problem is discovered.

Checks that splits have been properly created.

Description

This helper function makes a few simple checks to identify obvious issues with the way that splits have been made in the supplied data.

Usage

check_splits(data)

Arguments

data

Dataframe which should have appropriate .split_id column.

Value

Returns NULL. Errors if a problem is discovered.

Checks that an appropriate weighting variable has been provided

Description

This helper function makes a few simple checks to identify obvious issues with the weights provided.

Usage

check_weights(data, weight_col)

Arguments

data

Dataframe of interest.

weight_col

Quoted name of weights column.

Value

Returns NULL. Errors if a problem is discovered.

Construct Pseudo-outcomes

Description

construct_pseudo_outcomes takes a dataset which has been prepared with plugin estimators of nuisance parameters and transforms these into a "pseudo-outcome": an unbiased estimator of the conditional average treatment effect under exogeneity.

Usage

construct_pseudo_outcomes(data, outcome, treatment, type = "dr")

Arguments

data

dataframe (already prepared with attach_config, make_splits, and produce_plugin_estimates)

outcome

Unquoted name of outcome variable.

treatment

Unquoted name of treatment variable.

type

String representing how to construct the pseudo-outcome. Valid values are "dr" (the default), "ipw" and "plugin". See "Details" for more discussion of these options.

Details

Taking averages of these pseudo-outcomes (or fitting a model to them) will approximate averages (or models) of the underlying treatment effect.

See Also

attach_config(), make_splits(), produce_plugin_estimates(), estimate_QoI()

Estimate Quantities of Interest

Description

estimate_QoI takes a dataframe already prepared with split IDs, plugin estimates and pseudo-outcomes and calculates the requested quantities of interest (QoIs).

Usage

estimate_QoI(data, ...)

Arguments

data

data frame (already prepared with attach_config, make_splits, produce_plugin_estimates and construct_pseudo_outcomes)

...

Unquoted names of moderators to calculate QoIs for.

Details

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

See Also

attach_config(), make_splits(), produce_plugin_estimates(), construct_pseudo_outcomes(),

Examples

library("dplyr")
if(require("palmerpenguins")) {
data(package = 'palmerpenguins')
penguins$unitid = seq_len(nrow(penguins))
penguins$propensity = rep(0.5, nrow(penguins))
penguins$treatment = rbinom(nrow(penguins), 1, penguins$propensity)
cfg <- basic_config() %>% 
add_known_propensity_score("propensity") %>%
add_outcome_model("SL.glm.interaction") %>%
remove_vimp()
attach_config(penguins, cfg) %>%
make_splits(unitid, .num_splits = 4) %>%
produce_plugin_estimates(outcome = body_mass_g, treatment = treatment, species, sex) %>%
construct_pseudo_outcomes(body_mass_g, treatment) %>%
estimate_QoI(species, sex)
}

Function to calculate diagnostics based on model outputs

Description

This function defines the calculations of common model diagnostics which are available.

Usage

estimate_diagnostic(data, label, prediction, diag_name, params)

Arguments

data

The full data frame with all auxilliary columns.

label

The (string) column name for the labels to evaluate against.

prediction

The (string) column name of predictions from the model to diagnose.

diag_name

The (string) name of the diagnostic to calculate. Currently available are "AUC", "MSE", "SL_coefs", "SL_risk", "RROC"

params

Any other necessary options to pass to the given diagnostic.

Examples

df <- dplyr::tibble(y = rbinom(100, 1, 0.5), p = rep(0.5, 100), w = rexp(100), u = 1:100)
attr(df, "weights") <- "w"
attr(df, "identifier") <- "u"
estimate_diagnostic(df, "y", "p", "AUC")

Fits a treatment effect model using the appropriate settings

Description

This function prepares data, fits the appropriate model and returns the resulting estimates in a standardized format.

Usage

fit_effect(full_data, weight_col, fx_col, ..., .Model_cfg)

Arguments

full_data

The full dataset of interest for the modelling problem.

weight_col

The unquoted weighting variable name to use in model fitting.

fx_col

The unquoted column name of the pseudo-outcome.

...

The unquoted names of covariates to use in the model.

.Model_cfg

A Model_cfg object configuring the appropriate model type to use.

Value

A list with one element, fx. This element contains a Predictor object of the appropriate subclass corresponding to the Model_cfg fit to the data.

Fit a predictor for treatment effects

Description

This function predicts treatment effects in a second stage model.

Usage

fit_fx_predictor(full_data, weights, psi_col, ..., .pcate.cfg, .Model_cfg)

Arguments

full_data

The full original data with all auxilliary columns.

weights

Weights to be used in the analysis.

psi_col

The unquoted column name of the calculated pseudo-outcome.

...

Covariate data, passed in as the unquoted names of columns in full_data

.pcate.cfg

A PCATE_cfg object describing what PCATEs to calculate (and how)

.Model_cfg

A Model_cfg object describing how the effect model should be estimated.

Value

A list with two items:

See Also

Model_cfg, PCATE_cfg

Fits a plugin model using the appropriate settings

Description

This function prepares data, fits the appropriate models and returns the resulting estimates in a standardized format.

Usage

fit_plugin(full_data, weight_col, outcome_col, ..., .Model_cfg)

Arguments

full_data

The full dataset of interest for the modelling problem.

weight_col

The unquoted weighting variable name to use in model fitting.

outcome_col

The unquoted column name to use as a label for the supervised learning problem.

...

The unquoted names of covariates to use in the model.

.Model_cfg

A Model_cfg object configuring the appropriate model type to use.

Value

A new Predictor object of the appropriate subclass corresponding to the Model_cfg fit to the data.

Fits a propensity score model using the appropriate settings

Description

This function prepares data, fits the appropriate model and returns the resulting estimates in a standardized format.

Usage

fit_plugin_A(full_data, weight_col, a_col, ..., .Model_cfg)

Arguments

full_data

The full dataset of interest for the modelling problem.

weight_col

The unquoted weighting variable name to use in model fitting.

a_col

The unquoted column name of the treatment.

...

The unquoted names of covariates to use in the model.

.Model_cfg

A Model_cfg object configuring the appropriate model type to use.

Value

A list with one element, ps. This element contains a Predictor object of the appropriate subclass corresponding to the Model_cfg fit to the data.

Fits a T-learner using the appropriate settings

Description

This function prepares data, fits the appropriate model and returns the resulting estimates in a standardized format.

Usage

fit_plugin_Y(full_data, weight_col, y_col, a_col, ..., .Model_cfg)

Arguments

full_data

The full dataset of interest for the modelling problem.

weight_col

The unquoted weighting variable name to use in model fitting.

y_col

The unquoted column name of the outcome.

a_col

The unquoted column name of the treatment.

...

The unquoted names of covariates to use in the model.

.Model_cfg

A Model_cfg object configuring the appropriate model type to use.

Value

A list with two elements, mu1 and mu0 corresponding to the models fit to the treatment and control potential outcomes, respectively. Each is a new Predictor object of the appropriate subclass corresponding to the the Model_cfg fit to the data.

Removes rows which have missing data on any of the supplied columns.

Description

This function removes rows with missingness based on the columns provided. If rows are dropped, a message is displayed to the user to inform them of this fact.

Usage

listwise_deletion(data, ...)

Arguments

data

The dataset from which to drop cases which are not fully observed.

...

Unquoted column names which must be non-missing. Missingness in these columns will result in dropped observations. Missingness in other columns will not.

Value

The original data with all observations which are fully observed.

Define splits for cross-fitting

Description

This takes a dataset, a column with a unique identifier and an arbitrary number of covariates on which to stratify the splits. It returns the original dataset with an additional column .split_id corresponding to an identifier for the split.

Usage

make_splits(data, identifier, ..., .num_splits)

Arguments

data

dataframe

identifier

Unquoted name of unique identifier column

...

variables on which to stratify (requires that quickblock be installed.)

.num_splits

number of splits to create. If VIMP is requested in QoI_cfg, this must be an even number.

Details

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

Value

original dataframe with additional .split_id column

See Also

attach_config(), produce_plugin_estimates(), construct_pseudo_outcomes(), estimate_QoI()

Examples

library("dplyr")
if(require("palmerpenguins")) {
data(package = 'palmerpenguins')
penguins$unitid = seq_len(nrow(penguins))
penguins$propensity = rep(0.5, nrow(penguins))
penguins$treatment = rbinom(nrow(penguins), 1, penguins$propensity)
cfg <- basic_config() %>% 
add_known_propensity_score("propensity") %>%
add_outcome_model("SL.glm.interaction") %>%
remove_vimp()
attach_config(penguins, cfg) %>%
make_splits(unitid, .num_splits = 4) %>%
produce_plugin_estimates(outcome = body_mass_g, treatment = treatment, species, sex) %>%
construct_pseudo_outcomes(body_mass_g, treatment) %>%
estimate_QoI(species, sex)
}

Prediction for an SL.glmnet object

Description

Prediction for the glmnet wrapper.

Usage

## S3 method for class 'SL.glmnet.interaction'
predict(
  object,
  newdata,
  remove_extra_cols = TRUE,
  add_missing_cols = TRUE,
  ...
)

Arguments

object

Result object from SL.glmnet

newdata

Dataframe or matrix that will generate predictions.

remove_extra_cols

Remove any extra columns in the new data that were not part of the original model.

add_missing_cols

Add any columns from original data that do not exist in the new data, and set values to 0.

...

Any additional arguments (not used).

See Also

SuperLearner::SL.glmnet

Estimate models of nuisance functions

Description

This takes a dataset with an identified outcome and treatment column along with any number of covariates and appends three columns to the dataset corresponding to an estimate of the conditional expectation of treatment (.pi_hat), along with the conditional expectation of the control and treatment potential outcome surfaces (.mu0_hat and .mu1_hat respectively).

Usage

produce_plugin_estimates(data, outcome, treatment, ..., .weights = NULL)

Arguments

data

dataframe (already prepared with attach_config and make_splits)

outcome

Unquoted name of the outcome variable.

treatment

Unquoted name of the treatment variable.

...

Unquoted names of covariates to include in the models of the nuisance functions.

.weights

Unquoted name of weights column. If NULL, all analysis will assume weights are all equal to one and sample-based quantities will be returned.

Details

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

See Also

attach_config(), make_splits(), construct_pseudo_outcomes(), estimate_QoI()

Examples

library("dplyr")
if(require("palmerpenguins")) {
data(package = 'palmerpenguins')
penguins$unitid = seq_len(nrow(penguins))
penguins$propensity = rep(0.5, nrow(penguins))
penguins$treatment = rbinom(nrow(penguins), 1, penguins$propensity)
cfg <- basic_config() %>% 
add_known_propensity_score("propensity") %>%
add_outcome_model("SL.glm.interaction") %>%
remove_vimp()
attach_config(penguins, cfg) %>%
make_splits(unitid, .num_splits = 4) %>%
produce_plugin_estimates(outcome = body_mass_g, treatment = treatment, species, sex) %>%
construct_pseudo_outcomes(body_mass_g, treatment) %>%
estimate_QoI(species, sex)
}

Removes variable importance information

Description

This removes the variable importance quantity of interest from an HTE_cfg.

Usage

remove_vimp(hte_cfg)

Arguments

hte_cfg

HTE_cfg object to update.

Value

Updated HTE_cfg object

Examples

library("dplyr")
basic_config() %>%
   remove_vimp() -> hte_cfg

Partition the data into folds

Description

This takes a dataset and a split ID and generates two subsets of the data corresponding to a training set and a holdout.

Usage

split_data(data, split_id)

Arguments

data

dataframe

split_id

integer representing the split to construct

Value

Returns an R6 object HTEFold with three public fields: