Decision Trees for Uplift Modeling

Rzepakowski, Piotr; Jaroszewicz, Szymon

doi:10.1109/icdm.2010.62

Cited by 62 publications

(80 citation statements)

References 16 publications

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“…For example, Radcliffe and Surry [21] use a criterion based on a statistical test on the interaction term between the treatment indicator and the split variable. In [25], uplift decision trees have been presented which are more in line with modern machine learning algorithms. Splitting criteria are based on information theoretical measures, and a dedicated pruning strategy is presented.…”

Section: Previous Workmentioning

confidence: 94%

“…We subtract the area under the diagonal line from this value in order to obtain more meaningful numbers. More details on evaluating uplift models and on uplift curves can be found in [21,25]. Figure 5 shows uplift curves for the breast-cancer dataset for three of the uplift models used in the comparison (see below).…”

Section: Comparison On Benchmark Datasetsmentioning

confidence: 99%

“…Uplift random forests which use ensembles of trees from Rzepakowski and Jaroszewicz [25,27] with splitting criteria modified to include extra randomization have been described by Guelman et al [7]. A thorough analysis of various types of ensembles in the uplift setting can be found in [28].…”

Section: Previous Workmentioning

confidence: 99%

“…Multiclass problems are converted to binary problems with the majority class considered to be +1 and remaining classes −1. The procedure is described in detail in [25], where a table is given with the exact conditions used to split each dataset.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In [25], an approach has been proposed to split standard UCI datasets into treatment and control groups suitable for uplift modeling. The conversion is performed by first picking one of the data attributes which either has a causal meaning or splits the data evenly into two groups.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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$$L_p$$ L p -Support vector machines for uplift modeling

Zaniewicz

Jaroszewicz

2017

Knowl Inf Syst

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Uplift modeling is a branch of machine learning which aims to predict not the class itself, but the difference between the class variable behavior in two groups: treatment and control. Objects in the treatment group have been subjected to some action, while objects in the control group have not. By including the control group, it is possible to build a model which predicts the causal effect of the action for a given individual. In this paper, we present a variant of support vector machines designed specifically for uplift modeling. The SVM optimization task has been reformulated to explicitly model the difference in class behavior between two datasets. The model predicts whether a given object will have a positive, neutral or negative response to a given action, and by tuning a parameter of the model the analyst is able to influence the relative proportion of neutral predictions and thus the conservativeness of the model. Further, we extend L p -SVMs to the case of uplift modeling and demonstrate that they allow for a more stable selection of the size of negative, neutral and positive groups. Finally, we present quadratic and convex optimization methods for efficiently solving the two proposed optimization tasks.

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Section: Previous Workmentioning

confidence: 94%

Section: Comparison On Benchmark Datasetsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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$$L_p$$ L p -Support vector machines for uplift modeling

Zaniewicz

Jaroszewicz

2017

Knowl Inf Syst

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Uplift Modeling

2017

Profit‐Driven Business Analytics

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Learning and confirming a class of treatment responders in clinical trials

Talisa

Chang

2021

Statistics in Medicine

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Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of follow‐up success is to identify a subpopulation among whom treatment effects are estimated to be beneficial, and enrolling future studies from this subpopulation. In this article we study confirmable responder class (CRC) learning, where the objective is to learn in a random half of the dataset (training set) a subpopulation among whom the predicted conditional ATE (CATE) suggests clinically meaningful benefit, with maximum power of being confirmed via hypothesis test in the other half (test set). We studied a set of CRC learners across simulated datasets in which either all patients benefited, or only some did. Performance metrics included the rate of confirmation in the test set, and the classification accuracy of the CRC compared with the group with true CATE>0. In trials where all patients benefitted, only two learners were able to consistently identify most of the population as the CRC. One of these also performed especially well when only some patients benefitted, having relatively high confirmation rates, and showing robustness to the dimension of the covariate vector and population characteristics. This learner is based on cross‐validation, and is a possible avenue for further development of model selection criteria for CRC learning. We also show that the performance of all methods can be improved by using both halves of the original dataset as training and test sets in turn.

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Decision Trees for Uplift Modeling

Cited by 62 publications

References 16 publications

$$L_p$$ L p -Support vector machines for uplift modeling

$$L_p$$ L p -Support vector machines for uplift modeling

Uplift Modeling

Learning and confirming a class of treatment responders in clinical trials

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