Run posterior sampling for a causal bundle

run_mcmc_causal() executes the PS block (when enabled) and the two arm-specific outcome models prepared by build_causal_bundle, then returns a single "causalmixgpd_causal_fit" object.

Usage

run_mcmc_causal(
  bundle,
  show_progress = TRUE,
  quiet = FALSE,
  parallel_arms = FALSE,
  workers = NULL,
  timing = FALSE,
  z_update_every = NULL
)

Arguments

bundle

A "causalmixgpd_causal_bundle" from build_causal_bundle().

show_progress

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

quiet

Logical; if TRUE, suppress step messages and progress display.

parallel_arms

Logical; if TRUE, run control and treated outcome arms in parallel.

workers

Optional integer workers for parallel arm execution.

timing

Logical; if TRUE, return arm and total timings in $timing.

z_update_every

Integer >= 1 passed to arm-level outcome MCMC.

Value

A list of class "causalmixgpd_causal_fit" containing the fitted treated/control outcome models, optional PS fit, the original bundle, and timing metadata when requested.

Details

The fitted object contains the posterior draws needed to evaluate arm-level predictive distributions \(F_1(y \mid x)\) and \(F_0(y \mid x)\), followed by marginal or conditional causal contrasts. When PS = FALSE in the bundle, the PS block is skipped and outcome prediction uses only the original covariates.

See also

build_causal_bundle, mcmc, predict.causalmixgpd_causal_fit, ate, qte.

Examples

if (FALSE) { # \dontrun{
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal")
fit <- run_mcmc_causal(cb)
} # }