TSLiNGAM: DirectLiNGAM under heavy tails. (arXiv:2308.05422v1 [stat.ME])

One of the established approaches to causal discovery consists of combining
directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe
the functional dependencies of effects on their causes. Possible
identifiability of SCMs given data depends on assumptions made on the noise
variables and the functional classes in the SCM. For instance, in the LiNGAM
model, the functional class is restricted to linear functions and the
disturbances have to be non-Gaussian.

In this work, we propose TSLiNGAM, a new …

arxiv assumptions data dependencies discovery effects functional graphs instance noise scm variables