Machine learning-augmented instrumental variables

Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.