Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study

Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for prediction, there is only little guidance available on tuning ML learners for causal machine learning and how to select among different ML learners. In this paper, we empirically assess the relationship between the predictive performance of ML methods and the resulting causal estimation based on the Double Machine Learning (DML) approach …

causal inference econ.em guidance hyperparameter inference literature machine machine learning modern performance prediction predictive simulation stat.ml study tasks