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

Jiabei Yang1,2, Issa J Dahabreh3, Jon A Steingrimsson1

  • 1Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island.

Biometrics
|February 2, 2021
PubMed
Summary

We developed causal interaction tree (CIT) algorithms to identify patient subgroups with varying treatment effects in observational studies. These methods improve understanding of treatment effectiveness in diverse populations.

Keywords:
causal inferencedoubly robust estimatorsheterogeneity of treatment effectsmachine learningrecursive partitioning

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Area of Science:

  • Biostatistics
  • Causal Inference
  • Machine Learning

Background:

  • Identifying subgroups with heterogeneous treatment effects is crucial for personalized medicine.
  • Observational data presents challenges due to confounding factors.

Purpose of the Study:

  • Introduce causal interaction tree (CIT) algorithms for subgroup discovery in observational data.
  • Adapt classification and regression trees for heterogeneous treatment effect estimation.

Main Methods:

  • CIT algorithms extend traditional tree-based methods.
  • Utilize subgroup-specific treatment effect estimators: inverse probability weighting, g-formula, and doubly robust methods.
  • Derive asymptotic properties and construct splitting criteria for CIT.

Main Results:

  • Simulations demonstrate the performance of CIT algorithms.
  • Applied CIT to analyze right heart catheterization effectiveness in critically ill patients.

Conclusions:

  • CIT algorithms provide a robust framework for analyzing heterogeneous treatment effects in observational studies.
  • Enhances understanding of treatment variations across patient subgroups.