Subset Of Arms Research Articles

Qini Curves for Multi-Armed Treatment Rules

Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.

Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback

We study the problem of stochastic multiple-arm identification, where an agent sequentially explores a size- subset of arms (also known as a super arm) from given arms and tries to identify the best super arm. Most work so far has considered the semi-bandit setting, where the agent can observe the reward of each pulled arm or assumed each arm can be queried at each round. However, in real-world applications, it is costly or sometimes impossible to observe a reward of individual arms. In this study, we tackle the full-bandit setting, where only a noisy observation of the total sum of a super arm is given at each pull. Although our problem can be regarded as an instance of the best arm identification in linear bandits, a naive approach based on linear bandits is computationally infeasible since the number of super arms is exponential. To cope with this problem, we first design a polynomial-time approximation algorithm for a 0-1 quadratic programming problem arising in confidence ellipsoid maximization. Based on our approximation algorithm, we propose a bandit algorithm whose computation time is (log ), thereby achieving an exponential speedup over linear bandit algorithms. We provide a sample complexity upper bound that is still worst-case optimal. Finally, we conduct experiments on large-scale data sets with more than 10 super arms, demonstrating the superiority of our algorithms in terms of both the computation time and the sample complexity.

Effects of chlordiazepoxide on cued radial maze performance in rats.

Effects of chlordiazepoxide (CDP) were examined on the performance of rats in an eight-arm radial maze with four cued and food-baited arms. Two conditions were used; random, with cue location varying over trials, and constant, with the same subset of arms consistently cued. In rats pre-trained to a 60-70% efficiency level (Rewarded entries/Total entries X 100), the effects of CDP (2.5, 5.0 and 10.0 mg/kg, IP) differed according to condition. Efficiency was substantially reduced in the random condition, and types of error were undifferentiated. In the constant cue condition the post-drug drop in efficiency was less marked, and errors were selectively those of re-entry into rewarded arms. In both conditions there was a high incidence of error clusters involving re-entries into both rewarded and non-rewarded arms in the random condition, and rewarded arms in the constant condition. The results suggested that CDP induced a general disruption of information processing rather than a specific impairment of working memory, together with some response perseveration which could occur in the presence or absence of discrimination failure.