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Using regression tree ensembles to model interaction effects: a graphical approach

Author

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  • Fritz Schiltz
  • Chiara Masci
  • Tommaso Agasisti
  • Daniel Horn

Abstract

Multiplicative interaction terms are widely used in economics to identify heterogeneous effects and to tailor policy recommendations. The execution of these models is often flawed due to specification and interpretation errors. This article introduces regression trees and regression tree ensembles to model and visualize interaction effects. Tree-based methods include interactions by construction and in a nonlinear manner. Visualizing nonlinear interaction effects in a way that can be easily read overcomes common interpretation errors. We apply the proposed approach to two different datasets to illustrate its usefulness.

Suggested Citation

  • Fritz Schiltz & Chiara Masci & Tommaso Agasisti & Daniel Horn, 2018. "Using regression tree ensembles to model interaction effects: a graphical approach," Applied Economics, Taylor & Francis Journals, vol. 50(58), pages 6341-6354, December.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:58:p:6341-6354
    DOI: 10.1080/00036846.2018.1489520
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    Cited by:

    1. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    2. Thomas H. McInish & Olena Nikolsko‐Rzhevska & Alex Nikolsko‐Rzhevskyy & Irina Panovska, 2020. "Fast and slow cancellations and trader behavior," Financial Management, Financial Management Association International, vol. 49(4), pages 973-996, December.
    3. Agasisti, Tommaso & Barucci, Emilio & Cannistrà, Marta & Marazzina, Daniele & Soncin, Mara, 2023. "Online or on-campus? Analysing the effects of financial education on student knowledge gain," Evaluation and Program Planning, Elsevier, vol. 98(C).

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