Causal inference using antidotal variables

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.