Skewed Data Distributions and Homoscedasticity

[vdemchenko3]

Hi,

I'm wondering what's the best approach for data that is highly right-skewed. Is it best to take a log transform of it to make it more "normal" or does DirectLiNGAM deal with skewed data? The causal graphs are substantially different if I take the log and then normalise the data compared to only normalising the data and keeping the skewed distribution. I couldn't find the implementations of Hyvarinen & Smith 2013 for skewed data.

Also, my understanding is that LiNGAM is specifically made for non-Gaussian distributions, but I'm a bit confused about how this impacts the adjacency matrix computation using linear regression since from my understanding non-Gaussian distributions violate homoscedasticity.

Any clarity on these two topics would be greatly appreciated!