Support Vector Machines for Differential Prediction

Kuusisto, Finn; Costa, Vítor Santos; Nassif, Houssam; Burnside, Elizabeth S.; Page, David L.; Shavlik, Jude W.

doi:10.1007/978-3-662-44851-9_4

Cited by 29 publications

(25 citation statements)

References 19 publications

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“…To the best of our knowledge this is the first such detailed comparison of uplift models and one of very few theoretical results on uplift modeling. Our analysis contradicts the common belief (Radcliffe and Surry 2011; Kuusisto et al 2014;Guelman et al 2012) that the double model approach is usually inferior to dedicated uplift models. Based on the results of our analysis we propose a modified estimator which combines the benefits of both approaches and which we believe to be the model of choice for uplift linear regression.…”

Section: Introductioncontrasting

confidence: 55%

“…Several works investigate combining such trees into ensembles (Guelman et al 2012;Sołtys et al 2014). Work on linear uplift models includes approaches based on class variable transformation (Lai 2006;Jaśkowski and Jaroszewicz 2012;Kane et al 2014;Pechyony et al 2013) used with logistic regression and approaches based on Support Vector Machines (Kuusisto et al 2014;Jaroszewicz 2013, Oct 2017). Those works only address the problem of classification and do not provide theoretical analyses which would clearly demonstrate the merits of each approach.…”

Section: Literature Overviewmentioning

confidence: 99%

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Linear regression for uplift modeling

Rudaś

Jaroszewicz

2018

Data Min Knowl Disc

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The purpose of statistical modeling is to select targets for some action, such as a medical treatment or a marketing campaign. Unfortunately, classical machine learning algorithms are not well suited to this task since they predict the results after the action, and not its causal impact. The answer to this problem is uplift modeling, which, in addition to the usual training set containing objects on which the action was taken, uses an additional control group of objects not subjected to it. The predicted true effect of the action on a given individual is modeled as the difference between responses in both groups. This paper analyzes two uplift modeling approaches to linear regression, one based on the use of two separate models and the other based on target variable transformation. Adapting the second estimator to the problem of regression is one of the contributions of the paper. We identify the situations when each model performs best and, contrary to several claims in the literature, show that the double model approach has favorable theoretical properties and often performs well in practice. Finally, based on our analysis we propose a third model which combines the benefits of both approaches and seems to be the model of choice for uplift linear regression. Experimental analysis confirms our theoretical results on both simulated and real data, clearly demonstrating good performance of the double model and the advantages of the proposed approach.

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Section: Introductioncontrasting

confidence: 55%

Section: Literature Overviewmentioning

confidence: 99%

Linear regression for uplift modeling

Rudaś

Jaroszewicz

2018

Data Min Knowl Disc

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“…The methods thus appear complementary to each other. The differential prediction SVM [15] also performs comparably with USVMs.…”

Section: Comparison On Benchmark Datasetsmentioning

confidence: 98%

“…Another type of uplift support vector machines was proposed in [15]. The approach is based on direct maximization of the area under the uplift curve.…”

Section: Previous Workmentioning

confidence: 99%

“…Since both those methods require probabilities to be predicted, the SVMs have been calibrated by training logistic regression models on their outputs. The differential prediction SVMs [15] are included under the name (Diff-pred-SVM). The next method included in the comparison, Treatment-SVM, is the standard classification approach, i.e., a support vector machine built only on the treatment group, ignoring the control cases.…”

Section: Comparison On Benchmark Datasetsmentioning

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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Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data

Lipkovich,

Svensson,

Ratitch

et al. 2024

Statistics in Medicine

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In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136‐196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.

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Support Vector Machines for Differential Prediction

Cited by 29 publications

References 19 publications

Linear regression for uplift modeling

Linear regression for uplift modeling

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

Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data

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