Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
DGX agentarXiv:2609.16971v1 Announce Type: cross Abstract: In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving re