bandit_invalid

Checks if the Thompson sampling probabilities either sum arbitrarily close
to 0 or if any of them are NA, indicating the direct calculation failed or did not converge.

internal

Simulates the results of completed randomized controlled
trials, as if they had been conducted as adaptive Multi-Arm Bandit
(MAB) trials instead. Augmented inverse probability weighted
estimation (AIPW), outlined by Hadad et al. (2021)
<doi:10.1073/pnas.2014602118>, is used to robustly estimate the
probability of success for each treatment arm under the adaptive
design. Provides customization options to simulate perfect/imperfect
information, stationary/non-stationary bandits, blocked treatment
assignments, along with control augmentation, and other hybrid
strategies for assigning treatment arms. The methods used in
simulation were inspired by Offer-Westort et al. (2021)
<doi:10.1111/ajps.12597>.

Noah Ochital

whatifbandit

Analyzing Randomized Experiments as Multi-Arm Bandits

Ryan T. Moore

bandit_invalid function

<dl><dt>bandit</dt>
<dd>a numeric vector of Thompson sampling probabilities.</dd></dl>

Arguments

Checks Validity of Thompson sampling probabilities — bandit_invalid

<dl>

<dt>bandit</dt>
<dd>a numeric vector of Thompson sampling probabilities.</dd>

</dl>

bandit_invalid: Checks Validity of Thompson sampling probabilities

Description

Usage

Value

Arguments