The variability of what "similar" means in this context, which transformations are allowed, and whether data points themselves or their feature representations

In machine learning, it is often valuable for models to be parsimonious orsparsefor a variety of reasons, from memory savings and computational speedups tomodel interpretability and generalizability.

Understanding treatment effect heterogeneity is vital for scientific and policy research.