  Transfer entropy (TE) captures the directed relationships between two
variables. Partial transfer entropy (PTE) accounts for the presence of all
confounding variables of a multivariate system and infers only about direct
causality. However, the computation of PTE involves high dimensional
distributions and thus may not be robust in case of many variables. In this
work, different variants of PTE are introduced, by building a reduced number of
confounding variables based on different scenarios in terms of their
interrelationships with the driving or response variable. Connectivity-based
PTE variants and utilizing the random forests (RF) methodology are evaluated on
synthetic time series. The empirical findings indicate the superiority of the
suggested variants over TE and PTE, especially in case of high dimensional
systems.
