Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation 事件

Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation arXiv:2605.10430v2 Announce Type: replace-cross Abstract: 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, wherea