Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'
This is an incremental analysis for researchers in statistical testing, highlighting practical trade-offs in dependence tests.
The paper compares MultiFIT to existing linear-time kernel tests based on HSIC, noting that both control test levels exactly at finite sample sizes, but finds performance limitations in MultiFIT's test power in experiments.
We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC). We highlight the fact that the levels of the kernel tests at any finite sample size can be controlled exactly, as it is the case with the level of MultiFIT. In our experiments, we observe some of the performance limitations of MultiFIT in terms of test power.