tmle_vimshift_delta: Outcome Under a Grid of Shifted Interventions via Delta...

In tlverse/tmle3shift: Targeted Learning of the Causal Effects of Stochastic Interventions

Outcome Under a Grid of Shifted Interventions via Delta Method

Description

O = (W, A, Y) W = Covariates A = Treatment (binary or categorical) Y = Outcome (binary or bounded continuous)

Usage

tmle_vimshift_delta(
  shift_fxn = shift_additive,
  shift_fxn_inv = shift_additive_inv,
  shift_grid = seq(-1, 1, by = 0.5),
  max_shifted_ratio = 5,
  weighting = c("identity", "variance"),
  ...
)

Arguments

shift_fxn	A function defining the type of shift to be applied to the treatment. For an example, see shift_additive.
shift_fxn_inv	A function defining the inverse of the type of shift to be applied to the treatment. For an example, see shift_additive_inv.
shift_grid	A numeric vector, specification of a selection of shifts (on the level of the treatment) to be applied to the intervention. This is a value passed to the functions above for computing various values of the outcome under modulated values of the treatment.
max_shifted_ratio	A numeric value indicating the maximum tolerance for the ratio of the counterfactual and observed intervention densities. In particular, the shifted value of the intervention is assigned to a given observational unit when the ratio of counterfactual intervention density to the observed intervention density is below this value.
weighting	A character indicating the type of weighting used for construction of the marginal structural model. "identity" applies the same weight to all individual estimates while "variance" applies weights based on the inverse variance of the estimate. It would be expected that variance-based weighting would yield more stable estimates of the parameter of the MSM. The default remains the identity weighting.
...	Additional arguments, passed to shift functions.

tlverse/tmle3shift documentation built on Sept. 24, 2024, 4:21 a.m.