Configuration of Quantities of Interest

Source: R/meta_cfg.R

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"))
)
#> Super Learner
#> Version: 2.0-28.1
#> Package created on 2021-05-04
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)
#> <HTE_cfg>
#>   Public:
#>     clone: function (deep = FALSE) 
#>     effect: SLEnsemble_cfg, Model_cfg, R6
#>     initialize: function (outcome = NULL, treatment = NULL, effect = NULL, qoi = NULL, 
#>     outcome: SLEnsemble_cfg, Model_cfg, R6
#>     qoi: QoI_cfg, R6
#>     treatment: SLEnsemble_cfg, Model_cfg, R6
#>     verbose: FALSE