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潜在多维聚类：推断与实践AI 解读

We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their…

存在干扰时的双重差分AI 解读

In many scenarios, such as the evaluation of place-based policies, potential outcomes depend not only on the unit’s own treatment but also on its neighbors’ treatments. Despite this, “difference-in-differences” (DID) type estimators typically ignore such interference among neighbors. I show in this paper that the canonical DID estimators generally fail to identify interesting causal effects in the presence of neighborhood interference. To incorporate interference structure…