Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice. However, the two communities often evaluate models under markedly different conditions. Methodological work typically relies on semi-simulated benchmarks and metrics that require counterfactual outcomes, whereas real-world applications rely on observable metrics based on ranking or test outcomes. Despite the well-known gap between methodological progress and practical deployment, the relationship between these evaluation regimes has not been examined systematically. We conduct a large-scale empirical study of treatment effect evaluation ac...
| Authors | George Panagopoulos |
|---|---|
| Primary category | cs.LG |
| Published | 11 May 2026 |
| Last updated at source | 23 May 2026 |
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