PAPD baseline (Imai's Population Average Prescriptive Difference, centered).

3-fold rotation of the centered IPW statistic from Eq. S18 of Li & Brunskill (2026). No reward model is fit; the reward_model argument is accepted for API symmetry with kpe and is ignored. n_folds is also ignored – PAPD always uses 3 folds.

Usage

papd(
  X,
  A,
  Y,
  propensity,
  n_shuffles = 100L,
  n_folds = 3L,
  contextual_policy = "policy_tree",
  best_arm = "ips",
  reward_model = "random_forest",
  policy_features = NULL,
  n_cores = 1L,
  seed = NULL,
  verbose = FALSE
)

Arguments

X

Numeric matrix or data frame of covariates, shape (n, d).

A

Integer vector of observed treatments, values in \(\{0, \ldots, K-1\}\). Factors are auto-coerced with a message.

Y

Numeric vector of observed outcomes.

propensity

Scalar, length-n vector, or (n, K) matrix. A scalar broadcasts; a vector is read as \(p(A_i \mid X_i)\) for the observed action only. Required when is_rct = TRUE (the default); ignored when is_rct = FALSE, where the propensity is fit on the nuisance folds and propensity may be left NULL.

n_shuffles

Number of Algorithm-1 repetitions (each with a different seed). Defaults to 100.

n_folds

Number of folds \(K\) the data is partitioned into per shuffle. Must be at least 2. Defaults to 6.

contextual_policy

One of "policy_tree" or "linear", or a list with fit and predict functions for a BYO contextual policy.

best_arm

One of "ips" or "simple", or a BYO list(fit, predict).

reward_model

One of "random_forest" or "linear", or a BYO list(fit, predict).

policy_features

Optional integer vector of 1-based column indices passed to the contextual policy. Defaults to all columns.

n_cores

Number of parallel workers for the shuffle loop. Defaults to 1 (sequential).

seed

Base random seed. Shuffle s uses seed + s.

verbose

If TRUE, emits progress messages.

Value

A "kpe" S3 object.