FX.Predictor: Predictor class for the cross-fit predictor of "partial"...
In tidyhte: Tidy Estimation of Heterogeneous Treatment Effects
FX.Predictor R Documentation
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
modelsA list of the K model fits
num_splitsThe number of folds used in cross-fitting.
num_mc_samplesThe 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.
covariatesThe unquoted names of the covariates used in the second-stage model.
model_classThe 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$new(models, num_splits, num_mc_samples, covariates, model_class)
Arguments
modelsA list of the K model fits.
num_splitsInteger number of cross-fitting folds.
num_mc_samplesInteger number of Monte-Carlo samples across the covariate
space. If this is larger than the sample size, then the whole dataset will be used.
covariatesThe unquoted names of the covariates.
model_classThe model class (in the sense of Model_cfg).
Method predict()
Predicts the PCATE surface over a particular covariate, returning a tibble with
the predicted HTE for every Monte-Carlo sample.
Usage
FX.Predictor$predict(data, covariate)
Arguments
dataThe full dataset
covariateThe unquoted covariate name for which to calculate predicted
treatment effects.
Returns
A tibble with columns:
-
covariate_value - The value of the covariate of interest
-
.hte - An estimated HTE
-
.id - The identifier for the original row (which had
covariate modified to covariate_value).
Method clone()
The objects of this class are cloneable with this method.
Usage
FX.Predictor$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
tidyhte documentation built on Aug. 14, 2023, 5:08 p.m.
R Package Documentation
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| FX.Predictor | R Documentation |
Predictor class for the cross-fit predictor of "partial" CATEs
Description
Predictor class for the cross-fit predictor of "partial" CATEs
Predictor class for the cross-fit predictor of "partial" CATEs
Details
The class makes it easier to manage the K predictors for retrieving K-fold cross-validated estimates, as well as to measure how treatment effects change when only a single covariate is changed from its "natural" levels (in the sense "natural" used by the direct / indirect effects literature).
Public fields
modelsA list of the K model fits
num_splitsThe number of folds used in cross-fitting.
num_mc_samplesThe number of samples to retrieve across the covariate space. If num_mc_samples is larger than the sample size, then the entire dataset will be used.
covariatesThe unquoted names of the covariates used in the second-stage model.
model_classThe model class (in the sense of
Model_cfg). For instance, a SuperLearner model will have model class "SL".
Methods
Public methods
Method new()
FX.predictor is a class which simplifies the management of a set of cross-fit
prediction models of treatment effects and provides the ability to get the "partial"
effects of particular covariates.
Usage
FX.Predictor$new(models, num_splits, num_mc_samples, covariates, model_class)
Arguments
modelsA list of the K model fits.
num_splitsInteger number of cross-fitting folds.
num_mc_samplesInteger number of Monte-Carlo samples across the covariate space. If this is larger than the sample size, then the whole dataset will be used.
covariatesThe unquoted names of the covariates.
model_classThe 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$predict(data, covariate)
Arguments
dataThe full dataset
covariateThe unquoted covariate name for which to calculate predicted treatment effects.
Returns
A tibble with columns:
-
covariate_value- The value of the covariate of interest -
.hte- An estimated HTE -
.id- The identifier for the original row (which hadcovariatemodified tocovariate_value).
Method clone()
The objects of this class are cloneable with this method.
Usage
FX.Predictor$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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