Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling

Abstract

We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones. Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting. Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift.

Support Vector Machines for Differential Prediction

“…Insight on the differences between older and younger patients is thus crucial in determining whether treatment is immediately necessary [19, 18]. …”

Exploring uplift modelling in direct marketing

“…By including the control group, it is possible to build a model which predicts the causal effect of the action for a given individual' (Jaroszewicz & Zaniewicz, 2017). An uplift model is also known as the differential response, true lift, impact, incremental impact, incremental response, net lift, net response, incremental lift, persuasion, or true response model (Larsen, 2010;Rzepakowski and Jaroszewicz, 2010;Radcliffe and Surry, 2011;Kubiak, 2012;Lund 2012;Nassif, 2013;Nasif, et al, 2013;Guillen et al, 2014;Kane et al, 2014;Lo and Pachamanova, 2015;Gubela et al. 2017).…”

A modified $χ^2$-test for uplift models with applications in marketing performance measurement

“…This definition of p C by the target group weighted control group response probabilities is crucial, as we will explain later. A discussion of the case pC := aC k will be given in We put êi = n i (p T − pC ) (11) as an estimator of the expectation of the additional responses in each individual campaign which is only valid under the null hypothesis (4) (note the missing indices on the right hand side). Additionally, we define…”