How to Reveal Hidden Treatment Effects with Heterogeneous Analysis and CATE Models
This article explains the concept of heterogeneous treatment effects (HTE), clarifies related terminology, outlines why HTE analysis matters for product decisions, and walks through dimension selection, statistical and machine‑learning methods—including ANOVA, causal trees, meta‑learners, and double‑machine‑learning—plus a practical MVP tool with code examples and future development directions.
