PCATE_cfg {tidyhte} | R Documentation |
Configuration of Partial CATEs
Description
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))