Honesty in Causal Forests: When It Helps and When It Hurts

ArXi:2506.13107v4 Announce Type: replace 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 subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages.