IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v101y2016icp226-235.html
   My bibliography  Save this article

Data Shared Lasso: A novel tool to discover uplift

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794731630041X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2016.02.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xuqian Yan & Carlo Locci & Florian Hiss & Astrid Nieße, 2024. "State-of-Health Estimation for Industrial H 2 Electrolyzers with Transfer Linear Regression," Energies, MDPI, vol. 17(6), pages 1-19, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Yaohui & Yang, Tianbao & Breheny, Patrick, 2021. "Hybrid safe–strong rules for efficient optimization in lasso-type problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    2. Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    3. Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
    4. Ana R. Leal & David Perez-Castillo & José Ernesto Amorós & Bryan W. Husted, 2020. "Municipal Green Purchasing in Mexico: Policy Adoption and Implementation Success," Sustainability, MDPI, vol. 12(20), pages 1-26, October.
    5. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
    6. Michoel, Tom, 2016. "Natural coordinate descent algorithm for L1-penalised regression in generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 60-70.
    7. Wang, Cheng & Chen, Haozhe & Jiang, Binyan, 2024. "HiQR: An efficient algorithm for high-dimensional quadratic regression with penalties," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    8. Allimuthu Elangovan & Nguyen Trung Duc & Dhandapani Raju & Sudhir Kumar & Biswabiplab Singh & Chandrapal Vishwakarma & Subbaiyan Gopala Krishnan & Ranjith Kumar Ellur & Monika Dalal & Padmini Swain & , 2023. "Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
    9. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    10. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
    11. Guo, Yi & Berman, Mark & Gao, Junbin, 2014. "Group subset selection for linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 39-52.
    12. Juan Carlos Laria & Line H. Clemmensen & Bjarne K. Ersbøll & David Delgado-Gómez, 2022. "A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    13. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    14. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    15. Martin Ravallion, 2022. "On the Gains from Tradable Benefits‐in‐kind: Evidence for Workfare in India," Economica, London School of Economics and Political Science, vol. 89(355), pages 770-787, July.
    16. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    17. Peter Abell & Ofer Engel, 2021. "Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?," Sociological Methods & Research, , vol. 50(4), pages 1842-1862, November.
    18. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    19. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    20. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:101:y:2016:i:c:p:226-235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.