arXiv stat.ML
· Papers
Honesty in Causal Forests: When It Helps and When It Hurts
arXiv:2506.13107v5 Announce Type: replace-cross Abstract: Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practice is honest estimation: dividing the data into two samples, one to define subgroup