  The multiscale Fisher's independence test (MULTIFIT hereafter) proposed by
Gorsky & Ma (2022) is a novel method to test independence between two random
vectors. By its design, this test is particularly useful in detecting local
dependence. Moreover, by adopting a resampling-free approach, it can easily
accommodate massive sample sizes. Another benefit of the proposed method is its
ability to interpret the nature of dependency. We congratulate the authors,
Shai Gorksy and Li Ma, for their very interesting and elegant work. In this
comment, we would like to discuss a general framework unifying the MULTIFIT and
other tests and compare it with the binary expansion randomized ensemble test
(BERET hereafter) proposed by Lee et al. (In press). We also would like to
contribute our thoughts on potential extensions of the method.
