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Machine learning for causal inference in economics

Author

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  • Anthony Strittmatter

    (UniDistance Suisse, Switzerland)

Abstract

Machine learning (ML) improves economic policy analysis by addressing the complexity of modern data. It complements traditional econometric methods by handling numerous control variables, managing interactions and non-linearities flexibly, and uncovering nuanced differential causal effects. However, careful validation and awareness of limitations such as risk of bias, transparency issues, and data requirements are essential for informed policy recommendations.

Suggested Citation

  • Anthony Strittmatter, 2025. "Machine learning for causal inference in economics," IZA World of Labor, Institute of Labor Economics (IZA), pages 516-516, April.
    • Handle: RePEc:iza:izawol:journl:2025:n:516
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