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

In nhejazi/haldensify: Highly Adaptive Lasso Conditional Density Estimation

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

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)
W3 <- rpois(n_obs, 3)
A <- rpois(n_obs, 3 * W1 - W2 + 2 * W1 * W2 + 4)
Y <- rbinom(n_obs, 1, plogis(A + W1 + W2 - W3 - W1 * W3))

# fit the IPW estimator
est_ipw <- ipw_shift(
  W = cbind(W1, W2, W3), A = A, Y = Y,
  delta = 0.5, cv_folds = 2L,
  n_bins = 5L, bin_type = "equal_range",
  lambda_seq = exp(seq(-1, -10, length = 100L)),
  # arguments passed to hal9001::fit_hal()
  max_degree = 3,
  smoothness_orders = 0,
  num_knots = NULL,
  reduce_basis = 1 / sqrt(n_obs)
)
confint(est_ipw)

nhejazi/haldensify documentation built on Feb. 23, 2024, 8:25 a.m.