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Heterogeneous Treatment Effect-based Random Forest: HTERF

Author

Listed:
  • Jocteur, Bérénice-Alexia
  • Maume-Deschamps, Véronique
  • Ribereau, Pierre

Abstract

Estimates of causal effects are needed to answer what-if questions about shifts in policy, such as new treatments in pharmacology or new pricing strategies for business owners. A new non-parametric approach is proposed to estimate the heterogeneous treatment effect based on random forests (HTERF). The potential outcome framework with unconfoundedness shows that the HTERF is pointwise almost surely consistent with the true treatment effect. Interpretability results are also presented.

Suggested Citation

  • Jocteur, Bérénice-Alexia & Maume-Deschamps, Véronique & Ribereau, Pierre, 2024. "Heterogeneous Treatment Effect-based Random Forest: HTERF," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:csdana:v:196:y:2024:i:c:s0167947324000549
    DOI: 10.1016/j.csda.2024.107970
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    Full references (including those not matched with items on IDEAS)

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