Double machine learning with gradient boosting and its application to the Big N audit quality effect

In this paper, we study the double machine learning (DML) approach of Chernozhukov et al. (2018) for estimating average treatment effect and apply this approach to examine the Big N audit quality effect in the accounting literature. This approach relies on machine learning methods and is suitable when a high dimensional nuisance function with many covariates is present in the model. This approach does not suffer from the "regularization bias" when a learning method with a proper convergence rate is used. We demonstrate by simulations that, for the DML approach, the gradient boosting method is fairly robust and to be preferred to other methods, such as regression tree, random forest, support vector regression machine, and the conventional Nadaraya–Watson nonparametric estimator. We then apply the DML approach with gradient boosting to estimate the Big N effect. We find that Big N auditors have a positive effect on audit quality and that this effect is not only statistically significant but also economically important. We further show that, in contrast to the results of propensity score matching, our estimates of said effect are quite robust to the hyper-parameters in the gradient boosting algorithm.