3 research outputs found
Designing Randomized Experiments to Predict Unit-Specific Treatment Effects
Typically, a randomized experiment is designed to test a hypothesis about the
average treatment effect and sometimes hypotheses about treatment effect
variation. The results of such a study may then be used to inform policy and
practice for units not in the study. In this paper, we argue that given this
use, randomized experiments should instead be designed to predict unit-specific
treatment effects in a well-defined population. We then consider how different
sampling processes and models affect the bias, variance, and mean squared
prediction error of these predictions. The results indicate, for example, that
problems of generalizability (differences between samples and populations) can
greatly affect bias both in predictive models and in measures of error in these
models. We also examine when the average treatment effect estimate outperforms
unit-specific treatment effect predictive models and implications of this for
planning studies.Comment: 46 pages, 3 figure
Clustered Linear Contextual Bandits with Knapsacks
In this work, we study clustered contextual bandits where rewards and
resource consumption are the outcomes of cluster-specific linear models. The
arms are divided in clusters, with the cluster memberships being unknown to an
algorithm. Pulling an arm in a time period results in a reward and in
consumption for each one of multiple resources, and with the total consumption
of any resource exceeding a constraint implying the termination of the
algorithm. Thus, maximizing the total reward requires learning not only models
about the reward and the resource consumption, but also cluster memberships. We
provide an algorithm that achieves regret sublinear in the number of time
periods, without requiring access to all of the arms. In particular, we show
that it suffices to perform clustering only once to a randomly selected subset
of the arms. To achieve this result, we provide a sophisticated combination of
techniques from the literature of econometrics and of bandits with constraints