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Modeling heterogeneous treatment effects in the presence of endogeneity

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  • Giacomo Benini
  • Stefan Sperlich

Abstract

An inappropriate handling of cross-sectional heterogeneity renders estimates of causal effects inaccurate and uninformative. The present paper discusses how the direct modeling of cross-sectional differences via semiparametric models represents a useful bridge between a statistical approach, where the conditional distribution of the dependent variable returns any value of the outcome given any value of the explanatory variables, and an econometric analysis, where functions and parameters have direct policy implications. The explicit modeling of heterogeneity across different groups improves the quality of the estimates, mitigates their dependence upon the chosen instrumental variable, diminishes the self-selection problem, and fosters the acquisition of useful information for the entire sample.

Suggested Citation

  • Giacomo Benini & Stefan Sperlich, 2022. "Modeling heterogeneous treatment effects in the presence of endogeneity," Econometric Reviews, Taylor & Francis Journals, vol. 41(3), pages 359-372, May.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:3:p:359-372
    DOI: 10.1080/07474938.2021.1927548
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