Some interventions or population attributes negate the effects of a treatment. This paper shows that incorporating these, what we call antidotal variables (AV), into a causal treatment effects analysis can with one cross-sectional regression identify the true causal effect, in addition to possible biases from selectivity and SUTVA violations. Whereas we apply the AV technique to analyze the California Paid Family Leave program, it has applications beyond this example.