Honest estimation in causal forests can reduce CATE accuracy and require up to 27% more data, especially when effect heterogeneity is strong and detectable.
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Honesty in Causal Forests: When It Helps and When It Hurts
Honest estimation in causal forests can reduce CATE accuracy and require up to 27% more data, especially when effect heterogeneity is strong and detectable.