Evaluating the Effects of Financial Deregulation on Bank Risk using Double Machine Learning

Abstract

This work examines the causal impact of deregulation within the U.S. banking sector, fo- cusing on the rollback of a specific provision of the Dodd-Frank Act through the Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018 (EGRRCPA). Originally enacted in response to the 2008 financial crisis, the Dodd-Frank Act introduced extensive regulatory reforms aimed at mitigating systemic risk. However, the partial repeal of its provisions has prompted renewed interest in assessing the implications for bank risk and financial stability. Our research contributes to this growing body of work by employing recent developments in causal inference, particularly Double Machine Learning (DML), to more accurately estimate treatment effects. DML leverages machine learning algorithms to flexibly model both treatment and outcome processes, controlling for bias via orthogonaliza- tion techniques and sample-splitting strategies. By applying DML to panel data, we address the complexities of policy evaluation with panel data and aim to improve the robustness of causal estimates. We conduct a reanalysis of Chronopoulos et al. [12], comparing estimates produced using traditional fixed effects with linear regression models and those generated by modern machine learning based estimators. Furthermore, we investigate the implications of key implementation choices such as panel data transformation techniques, cross-fitting pro- cedures, and hyperparameter optimization on the performance and interpretability of DML in applied policy settings. Our work underscores the value of integrating modern computa- tional tools into empirical regulatory analysis, offering insights for policymakers and causal researchers.