Review for NeurIPS paper: A Class of Algorithms for General Instrumental Variable Models

The work provides a method based on modern machine learning for bounding causal effects under the instrumental variable graph and when both treatment and outcome variables are continuous. Overall, reviewers were positive about the paper, and I share the general assessment, this is a very nice and strong piece of work. Having said that, I will list some serious issues I found when reading the paper (the not so good part), which I expect the authors will take into account and reflect in the camera-ready version of the paper First, the paper's contribution is overstated, which is not needed due to the high quality of the work (!). For instance, the author says (line 35-36): "In this work, we develop algorithms to compute these bounds on causal effects over all IV models compatible with the data in a general continuous setting. "This is misleading since the work doesn't consider the most general setting.