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Data Shared Lasso: A novel tool to discover uplift

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  • Gross, Samuel M.
  • Tibshirani, Robert

Abstract

A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied to a sentiment analysis dataset to show its efficacy and interpretability. One particularly useful application is for finding sub-populations in a randomized trial for which an intervention (treatment) is beneficial, often called the uplift problem. Some new concepts are introduced that are useful for uplift analysis. The value is demonstrated in an application to a real world credit card promotion dataset. In this example, although sending the promotion has a very small average effect, by targeting a particular subgroup with the promotion one can obtain a 15% increase in the proportion of people who purchase the new credit card.

Suggested Citation

  • Gross, Samuel M. & Tibshirani, Robert, 2016. "Data Shared Lasso: A novel tool to discover uplift," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 226-235.
    • Handle: RePEc:eee:csdana:v:101:y:2016:i:c:p:226-235
    DOI: 10.1016/j.csda.2016.02.015
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    3. Robert Tibshirani & Jacob Bien & Jerome Friedman & Trevor Hastie & Noah Simon & Jonathan Taylor & Ryan J. Tibshirani, 2012. "Strong rules for discarding predictors in lasso‐type problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 245-266, March.
    4. Leo Guelman & Montserrat Guillen & Ana M. Pérez-Marín, 2014. "Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study," Working Papers 2014-06, Universitat de Barcelona, UB Riskcenter.
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    Cited by:

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    2. Zachary F. Fisher & Younghoon Kim & Barbara L. Fredrickson & Vladas Pipiras, 2022. "Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 1-29, June.
    3. E. Ollier & V. Viallon, 2017. "Regression modelling on stratified data with the lasso," Biometrika, Biometrika Trust, vol. 104(1), pages 83-96.

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