What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning

This research commentary introduces double/debiased machine learning (DML), a novel methodological framework, to the information systems (IS) research community, demonstrating its power to address the challenges of empirical model specifications. DML combines the flexibility of modern machine learning (ML) techniques with the rigor of semiparametric statistical theory, enabling effective modeling of complex functions alongside valid statistical inference. The paper provides an accessible and comprehensive overview of DML’s key elements—Neyman orthogonality, cross-fitting, and high-quality ML estimation—and their roles in achieving methodological flexibility and rigor. The versatility of DML is illustrated through applications in several empirical settings common in IS research, including standard linear regression with control covariates, instrumental variable regressions, difference in differences, and scenarios with ML-generated covariates. Comparative simulations and real data analyses show that DML outperforms traditional parametric and semiparametric methods, and they also illustrate the importance of DML’s key elements. Finally, we highlight potential misconceptions and pitfalls in applying DML and offer practical advice for empirical researchers. Given the increasing complexity of data and research questions in the IS field, DML offers a timely and powerful tool for empirical researchers. By promoting a deeper understanding and appropriate use of DML, this commentary aims to empower empirical research in IS.

History: Karthik Kannan, Senior Editor; Anuj Kumar, Associate Editor.

Funding: X. Mao is supported in part by the National Natural Science Foundation of China [Grants 72201150, 72322001, and 72293561] and the National Key R&D Program of China [Grant 2022ZD0116700]. B. Li’s research was supported by the National Natural Science Foundation of China [Grants 72171131 and 72133002].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.0888.