Da associação à explicação: fortalecendo o desenho de pesquisa e o raciocínio causal na pesquisa em contabilidade
DOI:
https://doi.org/10.14392/asaa.2026190101Palavras-chave:
Inferência Causal, desenho de pesquisa, econometria, contabilidade, mecanismos, mensuração, validadeResumo
Um editorial recente desta revista (Trevisan, 2024) destacou a crescente importância da inferência causal na pesquisa em contabilidade e os riscos de interpretar padrões correlacionais como relações causais. Com base nessa contribuição, este editorial avança a discussão ao deslocar o foco para etapas anteriores do processo: como os pesquisadores formulam perguntas, definem estimandos, articulam mecanismos, justificam escolhas de mensuração e constroem estratégias de identificação coerentes. Em vez de retomar estimadores quase-experimentais como uma caixa de ferramentas, organizamos o processo de pesquisa como um fluxo de desenho que pode sustentar inferência descritiva, preditiva, associativa e causal. Propomos um checklist prático para autores e pareceristas, discutimos modos recorrentes de falha em desenhos empíricos na contabilidade (controles, observações influentes e desalinhamento entre desenho e alegações) e conectamos escolhas metodológicas a normas de transparência e reprodutibilidade na área.
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Referências
Abadie, A., Diamond, A., & Hainmueller, J. (2010). Syn- thetic control methods for comparative case studies: Esti- mating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505.
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Iden- tification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455.
Angrist, J. D., & Pischke, J. (2009). Mostly harmless econo- metrics: An empiricist’s companion. Princeton University Press.
Berk, J. B., Harvey, C. R., & Hirshleifer, D. (2017). How to write an effective referee report and improve the scientific review process. Journal of Economic Perspectives, 31(1), 231–244.
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275.
Bloomfield, R. J. (2015). Rethinking managerial reporting.
Journal of Management Accounting Research, 27 (1), 139–
150.
Callaway, B., & Sant’Anna, P. H. C. (2021). Difference-in- differences with multiple time periods. Journal of Econo- metrics, 225(2), 200–230.
Cinelli, C., & Hazlett, C. (2020). Making sense of sensitiv- ity: Extending omitted variable bias. Journal of the Royal
Statistical Society Series B: Statistical Methodology, 82(1), 39–67.
Cinelli, C., Steegen, S., & Tuerlinckx, F. (2024). A crash course in good and bad controls. Sociological Methods & Research, 53(3), 1071–1104.
Costa, C. M., Galdi, F. C., Motoki, F. Y. S., & Sanchez,
J. M. (2016). Non-compliance in executive compensation disclosure: The Brazilian experience. Journal of Business Finance & Accounting, 43(3–4), 329–369.
Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.
da Silva, R. L. M., & Nardi, P. C. C. (2017). Full adoption of IFRSs in Brazil: Earnings quality and the cost of equity capital. Research in International Business and Finance, 42, 1057–1073.
de Chaisemartin, C., & D’Haultfœuille, X. (2020). Two- way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996.
Edmans, A. (2025). Learnings from 1000 rejections. Finan- cial Management, 54(2), 419–444.
Evans, J. H. I., Feng, M., Hoffman, V. B., Moser, D. V., & van der Stede, W. A. (2015). Points to consider when self-assessing your empirical accounting research. Con- temporary Accounting Research, 32(3), 1162–1192.
Funchal, B., & Monte-Mor, D. S. (2016). Corporate gov- ernance and credit access in Brazil: The Sarbanes-Oxley Act as a natural experiment. Corporate Governance: An International Review, 24(5), 528–547.
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328–331.
Gonçalves, T., Costa, C., Motoki, F. Y. S., & De Moura,
A. A. F. (2022). What influences the implementation of IFRS for SMEs? The Brazilian case. Accounting & Finance, 62(2), 2493–2526.
Gow, I. D., Larcker, D. F., & Reiss, P. C. (2016). Causal inference in accounting research. Journal of Accounting Research, 54(2), 477–523.
Hail, L., Lang, M., & Leuz, C. (2020). Reproducibility in accounting research: Views of the research community. Journal of Accounting Research, 58(2), 519–543.
Heckman, J. J., & Hotz, V. J. (1989). Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training. Journal of the American Statistical Association, 84(408), 862–874.
Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36(1), 49–67.
Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.
Holland, P. W. (1986). Statistics and causal inference. Jour- nal of the American Statistical Association, 81(396), 945– 960.
Hubbard, R., Haig, B. D., & Parsa, R. A. (2019). The limited role of formal statistical inference in scientific inference. The American Statistician, 73(sup1), 91–98.
Huntington-Klein, N., Arenas, A., Beam, E. A., Bertoni, M., Bloem, J. R., Burli, P., Chen, N., Grieco, P., Ekpe, G., Pugatch, T., Saavedra, M., & Stopnitzky, Y. (2021). The influence of hidden researcher decisions in applied microeconomics. Economic Inquiry, 59(3), 944–960.
Imbens, G. W. (2020). Potential outcome and directed acyclic graph approaches to causality: Relevance for em- pirical practice in economics. Journal of Economic Litera- ture, 58(4), 1129–1179.
Imbens, G. W., & Rubin, D. B. (2015). Causal inference for statistics, social, and biomedical sciences: An introduction. Cambridge University Press.
Kennedy, P. E. (2002). Sinning in the basement: What are the rules? The ten commandments of applied economet- rics. Journal of Economic Surveys, 16(4), 569–589.
Kondylis, F., & McKenzie, D. (2024). A curated list of our postings on technical topics—your one-stop shop for methodology [Accessed December 10, 2025].
Larcker, D. F., & Rusticus, T. O. (2010). On the use of instrumental variables in accounting research. Journal of Accounting and Economics, 49(3), 186–205.
Leamer, E. E. (1983). Let’s take the con out of economet- rics. American Economic Review, 73(1), 31–43.
Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281–355.
Leone, A. J., Minutti-Meza, M., & Wasley, C. E. (2019). Influential observations and inference in accounting re- search. The Accounting Review, 94(6), 337–364.
Leuz, C. (2022). Towards a design-based approach to ac- counting research. Journal of Accounting and Economics, 74(2-3), 101550.
Libby, R. (1981). Accounting and human information pro- cessing: Theory and applications. Prentice Hall.
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010). Negative controls: A tool for detecting confounding and bias in observational studies. Epidemiology, 21(3), 383– 388.
List, J. A. (2020). Non est disputandum de generalizability? A glimpse into the external validity trial (Working Paper No. 27535). National Bureau of Economic Research.
Mändli, F., & Rönkkö, M. (2025). To omit or to include? Integrating the frugal and prolific perspectives on control variable use. Organizational Research Methods, 28(1), 114–137.
Motoki, F. Y. S., Murgia, L. M., & Funchal, B. (forthcoming). The power and peril of nudging non-compliant firms: A field experiment on tax compliance [Conditionally accepted]. Journal of Accounting and Public Policy.
Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Eco- nomic Statistics, 37 (2), 187–204.
Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods & Research, 27 (2), 226–
284.
Roth, J. (2022). Pretest with caution: Event-study esti- mates after testing for parallel trends. American Economic Review: Insights, 4(3), 305–322.
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Economet- rics, 235(2), 2218–2244.
Rubin, D. B. (1974). Estimating causal effects of treat- ments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.
Sampaio, J. O., Gallucci Netto, H., Brunassi Silva, V. A., & Schiozer, R. F. (2020). Mandatory IFRS adoption, corpo- rate governance and firm value [Working paper].
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.
Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4, 1208–1214.
Sofer, T., Richardson, D. B., Colicino, E., Schwartz, J., & Tchetgen Tchetgen, E. J. (2016). On negative outcome control of unobserved confounding as a generalization of difference-in-differences. Statistical Science, 31(3), 348–
361.
Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel,
W. (2016). Increasing transparency through a multiverse
analysis. Perspectives on Psychological Science, 11(5), 702–712.
Sun, L., & Abraham, S. (2021). Estimating dynamic treat- ment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199.
Tchetgen Tchetgen, E. J. (2014). The control outcome calibration approach for causal inference with unobserved confounding. American Journal of Epidemiology, 179(5), 633–640.
Trevisan, G. (2024). Quebrando o paradigma: Da corre- lação à inferência causal. Advances in Scientific and Ap- plied Accounting, 17 (3).
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p < 0.05”. The American Statis- tician, 73(sup1), 1–19.
Whited, T. M., Moon, T. S., Shipman, J. E., & Swanquist,
Q. T. (2022). Out of control: The (over)use of controls in accounting research. The Accounting Review, 97 (3), 393–
421.
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Copyright (c) 2026 Fabio Yoshio Suguri Motoki , Valdemar Pinho Neto, Victor Rangel
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