aw_estimate: Estimate policy value via non-contextual adaptive weighting.

In banditsCI: Bandit-Based Experiments and Policy Evaluation

Estimate policy value via non-contextual adaptive weighting.

Description

Estimates the value of a policy based on AIPW scores and a policy matrix using non-contextual adaptive weighting. If evalwts is not provided, uses equal weights for all observations.

Usage

aw_estimate(scores, policy, evalwts = NULL)

Arguments

scores	Numeric matrix. AIPW scores, shape [A, K], where A is the number of observations and K is the number of arms. Must not contain NA values.
policy	Numeric matrix. Policy matrix \pi(X_t, w), shape [A, K]. Must have the same shape as scores and must not contain NA values.
evalwts	Optional numeric vector. Non-contextual adaptive weights h_t, length A, or NULL. Default is NULL.

Value

Numeric scalar. Estimated policy value.

Examples

scores <- 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, byrow = TRUE)
policy <- matrix(c(0.2, 0.3, 0.5,
                   0.6, 0.1, 0.3,
                   0.4, 0.5, 0.1,
                   0.2, 0.7, 0.1), ncol = 3, byrow = TRUE)
aw_estimate(scores = scores, policy = policy, evalwts = c(0.5, 1, 0.5, 1.5))

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