Causal Inference Using Antidotal Variables

Tirthatanmoy Das and Solomon Polachek

No 2574, RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin)

Abstract: 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.

Keywords: Bias; California Paid Family Leave; Causality; Nullifying Effect; Spillover Effect (search for similar items in EconPapers)
JEL-codes: C18 C36 I38 J18 J38 (search for similar items in EconPapers)
Date: 2025-09
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rfberlin.com/wp-content/uploads/2025/09/25074.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crm:wpaper:2574

Access Statistics for this paper

More papers in RFBerlin Discussion Paper Series from ROCKWOOL Foundation Berlin (RFBerlin) Contact information at EDIRC.
Bibliographic data for series maintained by Moritz Lubczyk () and Matthew Nibloe ().