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Conditional average treatment effects

cate.Rd

cate() evaluates treated-minus-control predictive means, or restricted means, at user-supplied covariate rows.

Usage

cate(
  fit,
  newdata = NULL,
  type = c("mean", "rmean"),
  cutoff = NULL,
  interval = "credible",
  level = 0.95,
  nsim_mean = 200L,
  show_progress = TRUE
)

Arguments

fit

A "causalmixgpd_causal_fit" object from run_mcmc_causal().

newdata

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

type

Character; type of mean treatment effect:

  • "mean" (default): ordinary mean CATE

  • "rmean": restricted-mean CATE (requires cutoff)

cutoff

Finite numeric cutoff for restricted mean; required for type = "rmean", ignored otherwise.

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).

nsim_mean

Number of posterior predictive draws used by simulation-based mean targets. Ignored for analytical ordinary means.

show_progress

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

Value

An object of class "causalmixgpd_ate" containing the CATE summary, optional intervals, 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 average treatment effect is $$\mathrm{CATE}(x) = E\{Y(1) \mid x\} - E\{Y(0) \mid x\}.$$

With type = "rmean", the estimand becomes the conditional restricted mean contrast $$E\{\min(Y(1), c) \mid x\} - E\{\min(Y(0), c) \mid x\},$$ which remains finite even when the ordinary mean is unstable under a heavy GPD tail. For outcome kernels with a finite analytical mean, the ordinary mean path is analytical within each posterior draw; rmean remains a separate simulation-based estimand.

This estimand is available only for conditional causal models with covariates. For marginal mean contrasts, use ate or att.

See also

ate, att, cqte, ate_rmean, 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)
cate(fit, newdata = X[1:5, ])
cate(fit, interval = "credible", level = 0.90)  # 90% CI
cate(fit, interval = "hpd")  # HPD intervals
cate(fit, interval = NULL)   # No intervals
} # }