Quantile treatment effects, marginal over the empirical covariate distribution

qte() returns the marginal quantile treatment effect implied by the causal fit.

Usage

qte(
  fit,
  probs = c(0.1, 0.5, 0.9),
  newdata = NULL,
  y = NULL,
  interval = "credible",
  level = 0.95,
  show_progress = TRUE
)

Arguments

fit

A "causalmixgpd_causal_fit" object from run_mcmc_causal().

probs

Numeric vector of probabilities in (0, 1) specifying the quantile levels of the outcome distribution to estimate treatment effects at.

newdata

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

y

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

interval

Character or NULL; type of credible interval:

• NULL: no interval

• "credible" (default): equal-tailed quantile intervals

• "hpd": highest posterior density intervals

level

Numeric credible level for intervals (default 0.95 for 95 percent CI).

show_progress

Logical; if TRUE, print step messages and render progress where supported.

Value

An object of class "causalmixgpd_qte" containing the marginal QTE summary, the probability grid, and the arm-specific predictive objects used in the aggregation. The returned object includes a top-level $fit_df data frame for direct extraction.

Details

The package computes $$\mathrm{QTE}(\tau) = Q_1^{m}(\tau) - Q_0^{m}(\tau),$$ where \(Q_a^{m}(\tau)\) is the arm-\(a\) posterior predictive marginal quantile obtained by averaging over the empirical training covariate distribution.

For unconditional causal models (X = NULL), this reduces to a direct contrast of the arm-level unconditional predictive distributions.

See also

qtt, cqte, ate, predict.causalmixgpd_causal_fit.

Examples

if (FALSE) { # \dontrun{
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal", components = 6)
fit <- run_mcmc_causal(cb, show_progress = FALSE)
qte(fit, probs = c(0.5, 0.9))
} # }