CausalMixGPD

Predict arm-specific and contrast-scale quantities from a causal fit

predict.causalmixgpd_causal_fit() is the causal counterpart to predict.mixgpd_fit. It coordinates the treated and control arm predictions so that both sides use the same covariate rows and the same PS adjustment.

Usage

# S3 method for class 'causalmixgpd_causal_fit'
predict(
  object,
  newdata = NULL,
  y = NULL,
  ps = NULL,
  id = NULL,
  type = c("mean", "quantile", "density", "survival", "prob", "sample"),
  p = NULL,
  index = NULL,
  nsim = NULL,
  interval = "credible",
  probs = c(0.025, 0.5, 0.975),
  store_draws = TRUE,
  nsim_mean = 200L,
  ncores = 1L,
  show_progress = TRUE,
  ...
)

Arguments

• object:

  A "causalmixgpd_causal_fit" object returned by run_mcmc_causal().

• newdata:

  Optional new data. If NULL, uses training design (if stored).

• y:

  Numeric vector of evaluation points (required for type="density" or "survival").

• ps:

  Optional numeric vector of propensity scores aligned with newdata. When provided, the supplied scores are used instead of recomputing them from the stored PS model (needed only for custom inputs).

• id:

  Optional identifier for prediction rows. Provide either a column name in newdata or a vector of length nrow(newdata). The id column is excluded from analysis.

• type:

  Prediction type:

  • "mean": posterior predictive mean treatment effect

  • "quantile": posterior predictive quantile treatment effect

  • "density": arm-specific posterior predictive densities

  • "survival": arm-specific posterior predictive survival functions

  • "prob": arm-specific posterior predictive probabilities

  • "sample": paired posterior predictive samples

• p:

  Numeric vector of probabilities for quantiles (required for type="quantile").

• index:

  Alias for p; numeric vector of quantile levels.

• nsim:

  Number of posterior predictive samples when type = "sample".

• interval:

  Character or NULL; type of credible interval:

  • NULL: no interval

  • "credible" (default): equal-tailed quantile intervals

  • "hpd": highest posterior density intervals

• probs:

  Quantiles for credible interval bands.

• store_draws:

  Logical; whether to store treatment-effect sample draws in the returned object when type = "sample".

• nsim_mean:

  Number of posterior predictive samples used by simulation-based mean targets. Ignored for analytical type = "mean"; still used for type = "rmean".

• ncores:

  Number of CPU cores to use for parallel prediction (if supported).

• show_progress:

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

• …:

  Additional arguments forwarded to per-arm predict.mixgpd_fit calls.

Value

For "mean" and "quantile", a causal prediction object whose $fit component reports treated-minus-control posterior summaries. For "density", "survival", and "prob", the $fit component contains side-by-side treated and control summaries evaluated on the supplied y grid. For "sample", the returned object contains paired treated, control, and treatment-effect posterior predictive samples. Sample outputs also include long-form data frames $fit_df, $trt_fit_df, and $con_fit_df for direct extraction.

Details

For each prediction row \x\, the function evaluates arm-specific posterior predictive quantities based on \F_1(y \mid x)\ and \F_0(y \mid x)\. Mean and quantile outputs are returned on the treatment-effect scale, while density, survival, and probability outputs retain both arm-specific curves. For outcome kernels with a finite analytical mean, the mean path uses analytical per-draw means; restricted means remain simulation-based.

If a PS model is stored in the fit, the same estimated score is supplied to both arms unless the user overrides it with ps. This is the main prediction entry point used internally by ate, qte, cate, and cqte.

See also

predict.mixgpd_fit, ate, qte, cate, cqte.

Examples

if (FALSE) { # \dontrun{
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal")
fit <- run_mcmc_causal(cb)
predict(fit, newdata = X[1:10, ], type = "quantile", index = c(0.25, 0.5, 0.75))
predict(fit, newdata = X[1:10, ], type = "mean", interval = "hpd")  # HPD intervals
predict(fit, newdata = X[1:10, ], type = "mean", interval = NULL)   # No intervals
} # }