Causal Inference for Qualitative Outcomes

Riccardo Di Francesco and Giovanni Mellace

Papers from arXiv.org

Abstract: Causal inference methods such as instrumental variables, regression discontinuity, and difference-in-differences are widely used to estimate treatment effects. However, their application to qualitative outcomes poses fundamental challenges, as standard causal estimands are ill-defined in this context. This paper highlights these issues and introduces an alternative framework that focuses on well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. We establish that standard identification assumptions are sufficient for identification and propose simple, intuitive estimation strategies that remain fully compatible with conventional econometric methods. To facilitate implementation, we provide an open-source R package, $\texttt{causalQual}$, which is publicly available on GitHub.

Date: 2025-02
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2502.11691 Latest version (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:arx:papers:2502.11691

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().