双边随机化设计中的因果推断：因子回归、双向聚类与协变量调整

中文摘要 AI 辅助

我们研究了涉及两个相互作用总体的随机化实验，例如市场中的买方和卖方。在我们考虑的双边实验中，我们分别且独立地对两个总体进行随机化。对于由每个总体中各一名成员组成的一对，两个分配共同决定四种暴露条件之一。在局部干扰假设下，我们考虑了一类广泛的线性估计量，包括总效应、交互效应以及买方侧和卖方侧的溢出效应。我们的第一个主要结果表明，研究者可以使用普通最小二乘法估计这些效应，并使用传统的双向聚类稳健方差估计器（按买方和卖方水平聚类）进行渐近有效的基于设计的推断。我们的第二个主要结果为单一线性估计量开发了一个更精确的方差估计器，该估计器更好地保留了买方和卖方维度内的依赖性，并且在渐近意义上比双向聚类估计器和现有替代方案更不保守。我们的第三个主要结果建立了协变量调整的理论，并推荐一种双向方差分析类型的协变量表示，以确保效率提升。

英文摘要

We study randomized experiments involving two interacting populations, such as buyers and sellers in a marketplace. In the two-sided experiments we consider, we randomize the two populations separately and independently. For a pair consisting of one member from each population, the two assignments jointly determine one of four exposure conditions. Under a local interference assumption, we consider a broad class of linear estimands, including total, interaction, and buyer- and seller-side spillover effects. Our first main result establishes that researchers can estimate these effects using ordinary least squares and conduct asymptotically valid design-based inference using the conventional two-way cluster-robust variance estimator, clustered at the buyers' and sellers' levels. Our second main result develops a sharper variance estimator for a single linear estimand that better preserves dependence within the buyer and seller dimensions and is asymptotically less conservative than the two-way clustered estimator and existing alternatives. Our third main result establishes the theory for covariate adjustment and recommends a two-way analysis-of-variance-type covariate representation to ensure efficiency gains.

发表机构

• Qiuzhen College, Tsinghua University（清华大学邱士书院）
• HSBC Business School, Peking University（北京大学汇丰商学院）
• Adobe Research（Adobe研究院）
• Department of Statistics, University of California, Berkeley（加州大学伯克利分校统计系）

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