Causal inference in two-sided randomization designs: factorial regression, two-way clustering, and covariate adjustment

2026-09-19

AI Summary

研究通过因子回归、双向聚类和协变量调整方法，解决双侧随机设计中的因果推断问题，适用于市场买卖双方等互动群体。

Abstract

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.

Problem

• two-sided randomization
• causal inference
• interaction effects
• spillover effects
• cluster-robust variance estimator

Innovation

• two-sided randomization
• ordinary least squares
• two-way cluster-robust variance estimator
• covariate adjustment