aw_scores: Compute AIPW/doubly robust scores.

In banditsCI: Bandit-Based Experiments and Policy Evaluation

Compute AIPW/doubly robust scores.

Description

Computes AIPW/doubly robust scores based on observed rewards, pulled arms, and inverse probability scores. If mu_hat is provided, compute AIPW scores, otherwise compute IPW scores.

Usage

aw_scores(yobs, ws, balwts, K, mu_hat = NULL)

Arguments

yobs	Numeric vector. Observed rewards. Must not contain NA values.
ws	Integer vector. Pulled arms. Must not contain NA values. Length must match yobs.
balwts	Numeric matrix. Inverse probability score 1[W_t=w]/e_t(w) of pulling arms, shape [A, K], where A is the number of observations and K is the number of arms. Must not contain NA values.
K	Integer. Number of arms. Must be a positive integer.
mu_hat	Optional numeric matrix. Plug-in estimator of arm outcomes, shape [A, K], or NULL. Must not contain NA values if provided.

Value

Numeric matrix. AIPW scores, shape [A, K].

Examples

aw_scores(yobs = c(0.5, 1, 0, 1.5),
          ws = c(1, 2, 2, 3),
          balwts = matrix(c(0.5, 2, 1, 0.5,
                            1, 1.5, 0.5, 1.5,
                            2, 1.5, 0.5, 1),
                            ncol = 3),
          K = 3,
          mu_hat = matrix(c(0.5, 0.8, 0.6, 0.3,
                            0.9, 0.2, 0.5, 0.7,
                            0.4, 0.8, 0.2, 0.6),
                            ncol = 3))

banditsCI documentation built on April 12, 2025, 1:42 a.m.