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.