AI 中文总结
研究溢出效应实验的最优设计与分析，刻画使最坏情况渐近方差最小化的处理分配分布和估计量，给出设计与分析问题的解决方案，框架在基准情形下有自然解并为一般网络提近似，半合成实验中方法降低标准误差、增加有效样本量。
AI 中文摘要
我们研究用于估计溢出效应的实验的最优设计与分析。假设已知（如线性）暴露映射，我们刻画了针对广泛的不可观测分布能使最坏情况渐近方差最小化的处理分配分布和基于回归的估计量。设计问题给出直观解决方案，即规划者在溢出信号强度与溢出变化扩散间权衡。分析问题产生一个简单的重新中心化工具变量估计量以最佳利用此变化。该框架在几个基准情形下给出自然解决方案，并为一般网络（包括二分设置）提出计算上可行的近似。我们在基于发展经济学两个应用的半合成实验中展示这些新工具。我们的方法在两个实验中都大幅降低了标准误差有效样本量增加了50%-100%或更多。
英文摘要
We study the optimal design and analysis of experiments for estimating spillover effects. Assuming a known (e.g., linear) exposure mapping, we characterize the treatment-assignment distribution and regression-based estimator that minimize worst-case asymptotic variance against a broad class of distributions of unobservables. The design problem yields an intuitive solution in which the planner trades off spillover signal strength against diffusion of spillover variation. The analysis problem yields a simple recentered instrumental variable estimator to best leverage this variation. This framework produces natural solutions in several benchmark cases - such as clustered exposure - and suggests computationally tractable approximations for general networks, including bipartite settings. We illustrate these new tools in semi-synthetic experiments based on two applications from development economics. Our approach yields large standard error reductions in both experiments, increasing effective sample sizes by 50-100% or more.
Comments70 pages, 6 figures, 6 tables
