Estimate the Exposure Mechanism via Generalized Propensity Score for Two Exposure Variable

joint_stoch_shift_est_g_exp(
  exposures,
  deltas,
  g_learner,
  covars,
  av,
  at,
  adaptive_delta,
  hn_trunc_thresh,
  use_multinomial,
  density_type,
  max_degree,
  n_bins,
  outcome_type
)

Arguments

exposures: A vector of characters labeling the exposure variables.
deltas: A numeric value identifying a shift in the observed value of the exposure under which observations are to be evaluated.
g_learner: Object containing a set of instantiated learners from sl3, to be used in fitting an ensemble model.
covars: A character labeling the covariate variables
av: A dataframe of validation data specific to the fold
at: A dataframe of training data specific to the fold
adaptive_delta: Whether to adaptively change the delta based on positivity determined from the clever covariate being below the hn_trunc_thresh level
hn_trunc_thresh: Truncation level of the clever covariate used in the adaptive delta method
use_multinomial: TRUE/FALSE for using multinomial for discretized exposure
density_type: Type of density estimator used
max_degree: Max degree of interaction used in the haldensify fit, if used.
n_bins: Number of bins to discretize outcome for haldensify if used.
outcome_type: Type of outcome variable

Value

A data.table with four columns, containing estimates of the generalized propensity score at a downshift (g(A - delta | W)), no shift (g(A | W)), an upshift (g(A + delta) | W), and an upshift of magnitude two (g(A + 2 delta) | W).

Details

Compute the propensity score (exposure mechanism) for the observed data, including the shift. This gives the propensity score for the observed data (at the observed A) the counterfactual shifted exposure levels (at A - delta, A + delta, and A + 2 * delta).