Estimate the Exposure Mechanism via Generalized Propensity Score

Usage

est_g_exp(
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(A)),
  fit_type = c("hal", "sl"),
  sl_learners_density = NULL,
  haldensify_args = list(grid_type = "equal_range", lambda_seq = exp(seq(-1, -13, length
    = 300)))
)

Arguments

A: A numeric vector of observed exposure values.
W: A numeric matrix of observed baseline covariate values.
delta: A numeric value identifying a shift in the observed value of the exposure under which observations are to be evaluated.
samp_weights: A numeric vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism est_samp.
fit_type: A character specifying whether to use Super Learner (from sl3) or the Highly Adaptive Lasso (from hal9001) to estimate the conditional exposure density.
sl_learners_density: Object containing a set of instantiated learners from sl3, to be used in fitting an ensemble model.
haldensify_args: A list of argument to be directly passed to haldensify when fit_type is set to "hal". Note that this invokes the Highly Adaptive Lasso instead of Super Learner and is thus only feasible for relatively small data sets.

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).