This paper examines the approaches accounting researchers use to draw causal inferences using observational (or non-experimental) 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); increased emphasis on structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.
Gow, Ian D. and Larcker, David F. and Reiss, Peter C., Causal Inference in Accounting Research (May 2016). Journal of Accounting Research, Vol. 54, No. 2, 2016; Rock Center for Corporate Governance at Stanford University Working Paper No. 217; Stanford University Graduate School of Business Research Paper No. 16-16. Available at SSRN: https://ssrn.com/abstract=2729565 or http://dx.doi.org/10.2139/ssrn.2729565
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