Double/Debiased Machine Learning for Economists: Practical Guidelines, Best Practices, and Common Pitfalls

In an era of increasing data complexity and volume, integrating machine learning (ML) into economics has become not only relevant but also essential. The advent of Double/Debiased Machine Learning (DML), an innovative framework for causal inference using ML, developed by Chernozhukov et al. (2018), has opened new avenues for utilizing ML in economics. However, the transition from theoretical concepts to applied research and practical implementation requires a clear, accessible guide. To bridge this gap, this paper first explains why generalizability should be the primary criterion for model evaluation, and then presents best practices and common pitfalls for building models with superior generalizability. Following this, practical, step-by-step guidelines for implementing DML are provided. This approach makes the integration of ML techniques in economic analysis more approachable and straightforward, enabling more accurate and robust causal inferences.