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Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data

Author

Listed:
  • Jiabei Yang
  • Issa J. Dahabreh
  • Jon A. Steingrimsson

Abstract

We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.

Suggested Citation

  • Jiabei Yang & Issa J. Dahabreh & Jon A. Steingrimsson, 2022. "Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data," Biometrics, The International Biometric Society, vol. 78(2), pages 624-635, June.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:2:p:624-635
    DOI: 10.1111/biom.13432
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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. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
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