Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
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arXiv:2609.16971v1 Announce Type: new Abstract: In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is often insufficient for making informed decisions; it is equally important…
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- 2026-09-16 04:00 · arXiv stat.ML
Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
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