Causal Inference Using Antidotal Variables
提出一种称为解毒变量的新方法，仅用一次横截面回归就能同时识别因果效应、溢出效应和选择性偏差，并应用于加州带薪家事假项目，发现处理效应比传统双重差分法大55%到70%。
This paper shows that incorporating what we call antidotal variables (AV) into a causal treatment effects analysis can with one cross-sectional regression identify the causal effect, the spillover effect, as well as possible biases from selectivity. We apply the AV technique to analyze leave taking arising from the California Paid Family Leave (CPFL) program. Our analysis yields between a 55% and 70% larger treatment effect than the traditional DID methods, which we attribute to confounding effects and spillovers, neither of which are found in traditional studies.