Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion
通过矩阵补全改进异质性处理效应估计的理论保证
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 represented as a matrix of all unit--time treatment effects. Estimating heterogeneous treatment effects can then be…