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Causal Inference Using Antidotal Variables

Author

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  • Tirthatanmoy Das
  • Solomon W. Polachek

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.

Suggested Citation

  • Tirthatanmoy Das & Solomon W. Polachek, 2025. "Causal Inference Using Antidotal Variables," RFBerlin Discussion Paper Series 2574, Rockwool Foundation Berlin (RF Berlin).
    • Handle: RePEc:crm:wpaper:2574
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    Keywords

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy

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