Causal inference in accounting research

• Autores: Ian D. Gow, Peter C. Reiss, David F. Larcker
• Localización: Journal of Accounting Research, ISSN-e 1475-679X, Vol. 54, Nº. 2, 2016, págs. 477-523
• Idioma: inglés
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• Dialnet Métricas: 4 Citas
• Resumen

  • This paper examines the approaches accounting researchers adopt to draw causal inferences using observational (or nonexperimental) data. The vast majority of accounting research papers draw causal inferences notwithstanding the well-known difficulties in doing so. While some recent papers seek to use quasi-experimental methods to improve causal inferences, these methods also make strong assumptions that are not always fully appreciated. We believe that accounting research would benefit from more in-depth descriptive research, including a greater focus on the study of causal mechanisms (or causal pathways) and increased emphasis on the structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.