est_mix_nuisance_params: Estimate nuisance parameters for each mixture interaction...

In blind-contours/CVtreeMLE: Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation

Estimate nuisance parameters for each mixture interaction identified

Description

For each mixture mixture interaction found, create a g estimator for the probability of being exposed to the rule thresholds, and a Q estimator for the outcome E(Y| A = a_mix, W). Get estimates of g and Q using the validation data and calculate the clever covariate used in the TMLE fluctuation step.

Usage

est_mix_nuisance_params(
  at,
  av,
  w,
  a,
  y,
  aw_stack,
  family,
  rules,
  parallel_cv,
  seed,
  h_aw_trunc_lvl
)

Arguments

at	Training data
av	Validation data
w	Vector of characters denoting covariates
y	The outcome variable
aw_stack	Super Learner library for fitting Q (outcome mechanism) and g (treatment mechanism)
family	Binomial or continuous
rules	Dataframe of rules found during the PRE fitting process
parallel_cv	TRUE/FALSE if cv parallelization is used
seed	Seed number
h_aw_trunc_lvl	Truncation level of the clever covariate (induces more bias to reduce variance)
no_mix_rules	TRUE/FALSE indicator for if no mixture rules were found

Value

A list of dataframes where the nuisance parameters are added to the raw data.

blind-contours/CVtreeMLE documentation built on June 22, 2024, 8:53 p.m.