Fit a causal two-arm Dirichlet process mixture with a spliced GPD tail

dpmgpd.causal() is the highest-level causal fitting wrapper. It builds or accepts a causal bundle, runs posterior sampling for the treated and control arms, and returns a single causal fit ready for prediction and effect estimation.

Usage

dpmgpd.causal(
  y = NULL,
  X = NULL,
  treat = NULL,
  data = NULL,
  mcmc = list(),
  formula = NULL,
  ...
)

Arguments

y

Either a response vector or a causal bundle object.

X

Optional design matrix/data.frame.

treat

Binary treatment indicator.

data

Optional data.frame used with formula.

mcmc

Named list of run arguments passed to mcmc() (including optional performance controls such as parallel_arms, workers, timing, and z_update_every).

formula

Optional formula.

...

Additional build arguments passed to build_causal_bundle.

Value

A fitted object of class "causalmixgpd_causal_fit".

Details

The arm-specific predictive distributions \(F_1(y \mid x)\) and \(F_0(y \mid x)\) inherit the spliced bulk-tail structure. Downstream causal estimands are computed as functionals of these two predictive laws, for example $$\mathrm{QTE}(\tau) = Q_1(\tau) - Q_0(\tau), \qquad \mathrm{ATE} = E(Y_1) - E(Y_0).$$

See also

build_causal_bundle, bundle, dpmix.causal, predict.causalmixgpd_causal_fit, ate, qte, cate, cqte.