A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied to a sentiment analysis dataset to show its efficacy and interpretability. One particularly useful application is for finding sub-populations in a randomized trial for which an intervention (treatment) is beneficial, often called the uplift problem. Some new concepts are introduced that are useful for uplift analysis. The value is demonstrated in an application to a real world credit card promotion dataset. In this example, although sending the promotion has a very small average effect, by targeting a particular subgroup with the promotion one can obtain a 15% increase in the proportion of people who purchase the new credit card.

中文翻译：

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数据共享套索：一种发现提升的新工具

为监督学习问题提供了一个模型，其中观察来自固定数量的预先指定的组，并且回归系数在组之间可能会发生稀疏变化。该模型跨越了每个组的单个模型和所有组的一个模型之间的连续体。由此产生的算法在设计时考虑了高维框架。该方法应用于情感分析数据集以显示其有效性和可解释性。一个特别有用的应用是在随机试验中寻找干预（治疗）有益的亚群，通常称为隆起问题。引入了一些对隆起分析有用的新概念。该值在现实世界信用卡促销数据集的应用程序中得到了证明。在这个例子中，