Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265779 
Year of Publication: 
2022
Series/Report no.: 
IZA Discussion Papers No. 15558
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
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.
Subjects: 
antidotal variables
causality
CPFL
JEL: 
C18
C36
I38
J18
J38
Document Type: 
Working Paper

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