Linear regression for uplift modeling

Multiple linear regression modeling for compositional data

Compositional data, containing relative information, occur regularly inmany disciplines and practical situations. Multivariate statistics methods including regression analysis have been adopted to model compositional data, but the existing research is still scattered and fragmented. This paper contributes to modeling the linear regression relationship for compositional data as both dependent an...

Pessimistic Uplift Modeling

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the prob...

Uplift modeling for clinical trial data

Traditional classification methods predict the class probability distribution conditional on a set of predictor variables. Uplift modeling, in contrast, tries to predict the difference between class probabilities in the treatment group (on which some action has been taken) and the control group (not subjected to the action) such that the model predicts the net effect of the action. Such an appr...

Uplift Modeling in Direct Marketing

Marketing campaigns directed to randomly selected customers often generate huge costs and a weak response. Moreover, such campaigns tend to unnecessarily annoy customers and make them less likely to answer to future communications. Precise targeting of marketing actions can potentially results in a greater return on investment. Usually, response models are used to select good targets. They aim ...

Log - Linear Modeling and Logistic Regression