Review for NeurIPS paper: A causal view of compositional zero-shot recognition

Weaknesses: * This method is most suitable for variables that have a single parent in the causal DAG -- the class label. This severely restricts the class of attributes that can be modeled and manifests in the paper as experiments with simple attributes (colors in AO-CLEVr, and materials in Zappos). In fact, prior work has noted that attributes (or other compositional modifiers) manifest very differently for different objects ([36] gives the examples from prior work: "fluffy" for towels vs. dogs, "ripe" for one fruit vs. another etc.). For these attributes, and many others, the data generating process is not so straightforward -- there are edges from both attribute labels and object labels to the core features. The authors do acknowledge this limitation in L326, however it is an important weakness to consider given that _difficult_ instances in real world datasets (where both object and attribute are parents of \phi_a for example) are fairly prevalent.