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

DOI10.1111/biom.13432zbMath1520.62385arXiv2003.03042OpenAlexW3127592970MaRDI QIDQ6079481

Unnamed Author, Issa J. Dahabreh, Jon Arni Steingrimsson

Publication date: 30 October 2023

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.03042

zbMATH Keywords

recursive partitioningmachine learningcausal inferencedoubly robust estimatorsheterogeneity of treatment effects

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Subgroup analysis using Bernoulli‐gated hierarchical mixtures of experts models

Cites Work

• Unnamed Item
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• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Weak convergence and empirical processes. With applications to statistics
• Recursive partitioning for heterogeneous causal effects
• The central role of the propensity score in observational studies for causal effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• Causal inference of interaction effects with inverse propensity weighting, G‐computation and tree‐based standardization
• Double/debiased machine learning for treatment and structural parameters

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