What Are We Weighting For?
区分了加权在描述性统计和因果效应估计中的三种不同动机：纠正异方差性、纠正内生抽样和识别未建模异质性下的平均偏效应，并指出实践中常误用加权。
When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does.