Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

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 to understand why the treatment effect is higher