Causal Inference in Accounting Research

Ian D. Gow, David F. Larcker and Peter C. Reiss
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Ian D. Gow: Harvard University
David F. Larcker: Stanford University
Peter C. Reiss: Stanford University

Research Papers from Stanford University, Graduate School of Business

Abstract: 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.

Date: 2016-02
New Economics Papers: this item is included in nep-acc
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