print.causalmixgpd_causal_bundle() gives a compact structural summary
of the pre-run causal bundle created by
build_causal_bundle.
# S3 method for class 'causalmixgpd_causal_bundle'
print(x, code = FALSE, max_code_lines = 200L, ...)
x:
A "causalmixgpd_causal_bundle" object.
code:
Logical; if TRUE, print generated NIMBLE code for each block.
max_code_lines:
Integer; maximum number of code lines to print when code=TRUE.
…:
Unused.
The input object (invisibly).
A causal bundle collects three pre-MCMC building blocks: the optional propensity-score model for \e(x) = \Pr(A = 1 \mid X = x)\, the control outcome model for \Y^0\, and the treated outcome model for \Y^1\. The printed output aligns those blocks side by side so the user can verify that the treated and control outcome specifications are coherent before sampling.
No causal estimand is computed at this stage. The bundle only records the structural assumptions that will later support estimands such as \E(Y^1 - Y^0 \mid X = x)\ or \Q_{Y^1}(\tau \mid X = x) - Q_{Y^0}(\tau \mid X = x)\.
summary.causalmixgpd_causal_bundle,
run_mcmc_causal,
ate,
qte.
if (FALSE) { # \dontrun{
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal")
print(cb)
} # }