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Causal Inference in Accounting Research
Author(s) -
GOW IAN D.,
LARCKER DAVID F.,
REISS PETER C.
Publication year - 2016
Publication title -
journal of accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.767
H-Index - 141
eISSN - 1475-679X
pISSN - 0021-8456
DOI - 10.1111/1475-679x.12116
Subject(s) - causal inference , accounting research , causal model , accounting , focus (optics) , observational study , inference , econometrics , computer science , management science , economics , artificial intelligence , mathematics , statistics , optics , physics
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.