Causal microeconometrics in accounting research

Abstract

Purpose: This paper reviews accounting research that employed causal inference methodology, with a focus on methods associated with causal microeconometrics (quasi-experimental). The reviewed papers were published in five leading accounting journals from 2017 through 2021. Methodology/approach: The research approach is a literature review of studies that apply the methodology of causal microeconometrics to accounting. The main section of the paper describes five methods: the treatment effects approach, propensity score matching, natural experiment, difference-in-differences estimators, and regression discontinuity design. The assumptions and limitations of each method are discussed, and selected examples of causal inference published in five leading accounting journals are provided. Findings: The study confirms the increasing frequency of the use of causal inference methodologies in accounting research. Sometimes referred to as quasi-experimental or causal microeconometric, these methods can provide a base for finding evidence of causality. However, there are limitations associated with each method. Practical implications: Statistical-econometric methodology in accounting research based on regression is rarely able to demonstrate causal relationships. This paper presents the pros and cons of applying causal inference methodologies in accounting. Originality/value: The paper’s value lies in: (1) introducing to the research community the growing presence of quasi-experimental causal methodologies in accounting, (2) presenting causal research in accounting using causal microeconometric methods, (3) identifying papers using these methods that were published in five leading accounting journals between 2017 and 2021, and (4) highlighting the challenges and the need for caution and due consideration in applying these methods.