Conditional quantile treatment effects

cqte() evaluates treated-minus-control predictive quantiles at user-supplied covariate rows.

Usage

cqte(
  fit,
  probs = c(0.1, 0.5, 0.9),
  newdata = 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

Optional data.frame or matrix of covariates for prediction. If NULL, uses the training covariates stored in fit.

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 CQTE summary, the probability grid, and the treated/control prediction objects used to construct the effect. The returned object includes a top-level $fit_df data frame for direct extraction.

Details

For each prediction row \(x\), the conditional quantile treatment effect is $$\mathrm{CQTE}(\tau, x) = Q_1(\tau \mid x) - Q_0(\tau \mid x).$$

This estimand is available only for conditional causal models with covariates. For marginal quantile contrasts over the empirical covariate distribution, use qte or qtt.

If the fit includes a PS block, the same PS adjustment is applied to both arm predictions before differencing.

See also

qte, qtt, cate, 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)
cqte(fit, probs = c(0.5, 0.9), newdata = X[1:5, ])
cqte(fit, probs = c(0.5, 0.9), interval = "credible", level = 0.90)  # 90% CI
cqte(fit, probs = c(0.5, 0.9), interval = "hpd")  # HPD intervals
cqte(fit, probs = c(0.5, 0.9), interval = NULL)   # No intervals
} # }