Reviews: Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

The authors develop new algorithms for instrumental variables based on the orthogonal ML technique. The paper considers the IV problem in terms of moment conditions and derive conditions where the target quantity of interest has a good rate of estimation. Then the evaluation is done on a number of experimental settings on semi-synthetic and real data. The theoretical contributions could be better explained instead of purely linking it away to existing literature; please consider adding full consistency proof for completeness. It would also serve the reader if a larger discussion the original double ML work and neyman-orthogonality was included.