Fit a causal two-arm Dirichlet process mixture without a GPD tail

dpmix.causal() fits a causal model with separate treated and control outcome mixtures and, when requested, a propensity score block. It is the bulk-only companion to dpmgpd.causal.

Usage

dpmix.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 resulting fit supports conditional outcome prediction \(F_a(y \mid x)\) for \(a \in \{0,1\}\), followed by causal functionals such as ate, qte, cate, and cqte.

See also

build_causal_bundle, bundle, dpmgpd.causal, predict.causalmixgpd_causal_fit, ate, qte.