confint.ipw_haldensify: Confidence Intervals for IPW Estimates of the Causal Effects...

In haldensify: Highly Adaptive Lasso Conditional Density Estimation

Description

Confidence Intervals for IPW Estimates of the Causal Effects of Stochatic Shift Interventions

Usage

## S3 method for class 'ipw_haldensify'
confint(object, parm = seq_len(object$psi), level = 0.95, ...)

Arguments

object	An object of class ipw_haldensify, produced by invoking the function ipw_shift, for which a confidence interval is to be computed.
parm	A numeric vector indicating indices of object$est for which to return confidence intervals.
level	A numeric indicating the nominal level of the confidence interval to be computed.
...	Other arguments. Not currently used.

Details

Compute confidence intervals for estimates produced by ipw_shift.

Value

A named numeric vector containing the parameter estimate from a ipw_haldensify object, alongside lower/upper Wald-style confidence intervals at a specified coverage level.

Examples

# simulate data
n_obs <- 50
W1 <- rbinom(n_obs, 1, 0.6)
W2 <- rbinom(n_obs, 1, 0.2)
A <- rnorm(n_obs, (2 * W1 - W2 - W1 * W2), 2)
Y <- rbinom(n_obs, 1, plogis(3 * A + W1 + W2 - W1 * W2))

# fit the IPW estimator
est_ipw_shift <- ipw_shift(
  W = cbind(W1, W2), A = A, Y = Y,
  delta = 0.5, n_bins = 3L, cv_folds = 2L,
  lambda_seq = exp(seq(-1, -10, length = 100L)),
  # arguments passed to hal9001::fit_hal()
  max_degree = 1,
  # ...continue arguments for IPW
  undersmooth_type = "gcv"
)
confint(est_ipw_shift)

haldensify documentation built on Feb. 10, 2022, 1:07 a.m.