Quantile treatment effects standardized to treated covariates

qtt() computes the quantile treatment effect on the treated.

Usage

qtt(
  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 QTT 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 estimand is $$\mathrm{QTT}(\tau) = Q_1^{t}(\tau) - Q_0^{t}(\tau),$$ where marginalization is over the empirical covariate distribution of the treated units only.

See also

qte, cqte, att.

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)
qtt(fit, probs = c(0.5, 0.9))
} # }