# Conditional quantile treatment effects

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

## Usage

``` r
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()`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/run_mcmc_causal.md).

- 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`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qte.md)
or
[`qtt`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qtt.md).

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

## See also

[`qte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qte.md),
[`qtt`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qtt.md),
[`cate`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/cate.md),
[`predict.causalmixgpd_causal_fit`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/predict.causalmixgpd_causal_fit.md).

## Examples

``` r
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
} # }
```