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Function to apply existing OTR(s) to external data

Source: R/apply_OTR.R
apply_OTR.Rd

Function to apply existing OTR(s) to external data

Usage

apply_OTR(
  df,
  CATE_models = NULL,
  d_pred = NULL,
  Y_name,
  A_name,
  W_list,
  Z_list = NULL,
  id_name = NULL,
  threshold = c("0.05"),
  nuisance_models = NULL,
  sl.library.outcome = NULL,
  sl.library.treatment = NULL,
  sl.library.missingness = NULL,
  k_folds = 5,
  ps_trunc_level = 0.01,
  outcome_type = "gaussian",
  truncate_CATE = "TRUE"
)

Arguments

df

dataframe containing external dataset to apply rule(s) to; must contain Z_list variables that are same as pre-trained rule(s)

CATE_models

list of CATE model(s) to apply to external dataset (if applying existing rule to new data)

d_pred

vector of binary treatment decisions (if have existing decisions and just want to estimate effects)

Y_name

name of outcome variable. Outcome variable in rule being applied should be the same as outcome variable in new data.

A_name

name of treatment variable. Treatment variable in rule being applied should be the same as the treatment variable in the new data.

W_list

character vector containing names of covariates in the dataframe used for nuisance models.

Z_list

character vector containing names of variables in df used to fit CATE model (variables used in treatment rule; must be same names as used in pre-fit CATE model(s); only required if applying rule)

id_name

name of participant ID variable if present in data

threshold

character vector of decision thresholds for CATE to determine OTR. Values should be positive if `Y_name` is desirable outcome, negative if `Y_name` is undesirable outcome. If threshold is 0, use +0 for desirable, -0 for undesirable.

nuisance_models

list of objects of class `Nuisance` containing outcome, treatment, and missingness SuperLearner models (only include if using pre-fit nuisance models)

sl.library.outcome

character vector of SuperLearner libraries to use to fit the outcome models (if fitting nuisance internally)

sl.library.treatment

character vector of SuperLearner libraries to use to fit the treatment models (if fitting nuisance internally)

sl.library.missingness

character vector of SuperLearner libraries to use to fit the missingness models (if fitting nuisance internally)

k_folds

integer number of folds to use for nuisance model cross-validation (must specify if fitting nuisance models here)

ps_trunc_level

numeric level below which propensity scores will be truncated (to avoid errors in computing AIPTW)

outcome_type

outcome_type specifying continuous (outcome_type = "gaussian") or binary (outcome_type = "binomial") outcome Y (if not providing pre-fit nuisance models)

truncate_CATE

logical to indicate if large CATE predictions should be truncated at -1 and 1 (default = TRUE)

Value

results

list of `Results` objects for each threshold. See description of `Results` object in `compute_estimates`

nuisance_models

list of `Nuisance` objects containing outcome, treatment, and missingness models used in each fold

CATE_models

CATE model used in each fold

Z_list

character vector containing names of variables in df used to fit CATE model (variables used in treatment rule)