论文标题
全球平均治疗效果的线性估计
Linear estimation of global average treatment effects
论文作者
论文摘要
我们研究了仅使用一些治疗的实验来估计治疗每个人群的平均因果效应的问题。我们考虑溢出物具有全球支持并以（广义的）距离缓慢衰减的设置。我们在估计器和设计上得出了最小速率，并表明它随溢出衰减的空间速率而增加。基于OLS回归的估计器，例如用于分析最近的大规模实验的估计器（尽管仅在降级后）是一致的，在DGP是线性时，达到最小值，并且在治疗簇小时比基于IPW的替代方案的速度更快，为OLS无处不在提供一个理由。当DGP非线性时，它们保持一致，但会缓慢收敛。我们进一步解决推理和带宽选择。应用于Egger等人研究的现金转移实验。 （2022）这些方法对消费产生了20％的估计影响。
We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. We consider settings where spillovers have global support and decay slowly with (a generalized notion of) distance. We derive the minimax rate over both estimators and designs, and show that it increases with the spatial rate of spillover decay. Estimators based on OLS regressions like those used to analyze recent large-scale experiments are consistent (though only after de-weighting), achieve the minimax rate when the DGP is linear, and converge faster than IPW-based alternatives when treatment clusters are small, providing one justification for OLS's ubiquity. When the DGP is nonlinear they remain consistent but converge slowly. We further address inference and bandwidth selection. Applied to the cash transfer experiment studied by Egger et al. (2022) these methods yield a 20% larger estimated effect on consumption.
