标题： 回归是否会产生有代表性的因果排序？ 显示英文标题

标题： Does Regression Produce Representative Causal Rankings?

摘要： 我们考察了在存在处理效应异质性的情况下，使用线性回归或其流行的双重机器学习变体——部分线性模型（PLM）时，基于估计效应对多种处理进行排序所面临的挑战。 我们通过示例说明，像PLM这样的线性模型执行的重叠加权可以产生加权平均处理效应（WATE），其排序与潜在平均处理效应（ATE）的排序不一致。 我们将此定义为排序反转，并在PLM下推导出排序反转的必要充分条件。 我们通过几个模拟研究得出排序反转发生的条件。 显示英文摘要

摘要： We examine the challenges in ranking multiple treatments based on their estimated effects when using linear regression or its popular double-machine-learning variant, the Partially Linear Model (PLM), in the presence of treatment effect heterogeneity. We demonstrate by example that overlap-weighting performed by linear models like PLM can produce Weighted Average Treatment Effects (WATE) that have rankings that are inconsistent with the rankings of the underlying Average Treatment Effects (ATE). We define this as ranking reversals and derive a necessary and sufficient condition for ranking reversals under the PLM. We conclude with several simulation studies conditions under which ranking reversals occur.