Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

arXiv:2609.16971 · stat.ML, cs.LG · Submitted 2026-09-15 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-15

Updated: 2026-09-15

Code: https://github.com/behr-group/intcf

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Causal Inference — 21× in this paper · explained in 4 episodes
• Interpretability — 21× in this paper · explained in 15 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes

Sources

• Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
• RealCause: Realistic Causal Inference Benchmarking

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