Causal classification concerns the estimation of the net effect of a treatment on an outcome of interest at the instance level, i.e., of the individual treatment effect (ITE). For binary treatment and outcome variables, causal classification models produce ITE estimates that essentially allow one to rank instances from a large positive effect to a large negative effect. Often, as in uplift modeling (UM), one is merely interested in this ranking, rather than in the ITE estimates themselves. In this regard, we investigate the potential of learning to rank (L2R) techniques to learn a ranking of the instances directly. We propose a unified formalization of different binary causal classification performance measures from the UM literature and explore how these can be integrated into the L2R framework. Additionally, we introduce a new metric for UM with L2R called the promoted cumulative gain (PCG). We employ the L2R technique LambdaMART to optimize the ranking according to PCG and show improved results over the use of standard L2R metrics and equal to improved results when compared with state-of-the-art UM. Finally, we show how L2R techniques can be used to specifically optimize for the top-kk fraction of the ranking in a UM context, however, these results do not generalize to the test set.

中文翻译：

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学习提升建模排名


因果分类涉及在实例级别上估计治疗对感兴趣结果的净效应，即个体治疗效果（ITE）。对于二元治疗和结果变量，因果分类模型产生 ITE 估计，本质上允许人们对实例从大的积极影响到大的消极影响进行排序。通常，就像在提升模型 (UM) 中一样，人们只对这一排名感兴趣，而不是对 ITE 估计本身感兴趣。在这方面，我们研究了学习排名（L2R）技术直接学习实例排名的潜力。我们提出了 UM 文献中不同二元因果分类性能度量的统一形式化，并探索如何将它们集成到 L2R 框架中。此外，我们还为具有 L2R 的 UM 引入了一个新指标，称为提升累积增益 (PCG)。我们采用 L2R 技术 LambdaMART 根据 PCG 优化排名，并显示出比使用标准 L2R 指标有所改进的结果，并且与最先进的 UM 相比，改进的结果相同。最后，我们展示了如何使用 L2R 技术来专门优化 UM 上下文中排名的 top-kk 部分，但是，这些结果并不能推广到测试集。