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We explore the opportunity of identifying potential sources of latent heterogeneity by employing a finite mixture regression model aimed at evaluating the causal effect of a treatment. Within each latent class of heterogeneity, the model describes the relationship between the outcome and the treatment, controlling for a set of covariates, and manages the endogeneity of the treatment. The proposed methodology can be applied to data of arbitrary nature, also allowing for the identification of possible heterogeneous effects.

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Unobserved Heterogeneity and Identification of Causal Effects Using Mixture Models

  • 会议论文

  • 发表日期：

    2025年

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  • Leonardo Genesin,
  • Marco Bertoni,
  • Adriano Paggiaro,
  • Giovanna Menardi

摘要

We explore the opportunity of identifying potential sources of latent heterogeneity by employing a finite mixture regression model aimed at evaluating the causal effect of a treatment. Within each latent class of heterogeneity, the model describes the relationship between the outcome and the treatment, controlling for a set of covariates, and manages the endogeneity of the treatment. The proposed methodology can be applied to data of arbitrary nature, also allowing for the identification of possible heterogeneous effects.