Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion 事件

Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion arXiv:2605.30319v1 Announce Type: cross Abstract: A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like "how does an intervention affect each unit," rather than only on average. We study this problem with panel-data where we observe $n$ units across $m$ times under unknown, non-uniform treatment assignments. The data in this setting is naturally repr