  Quantifying the directionality of information flow is instrumental in
understanding, and possibly controlling, the operation of many complex systems,
such as transportation, social, neural, or gene-regulatory networks. The
standard Transfer Entropy (TE) metric follows Granger's causality principle by
measuring the Mutual Information (MI) between the past states of a source
signal $X$ and the future state of a target signal $Y$ while conditioning on
past states of $Y$. Hence, the TE quantifies the improvement, as measured by
the log-loss, in the prediction of the target sequence $Y$ that can be accrued
when, in addition to the past of $Y$, one also has available past samples from
$X$. However, by conditioning on the past of $Y$, the TE also measures
information that can be synergistically extracted by observing both the past of
$X$ and $Y$, and not solely the past of $X$. Building on a private key
agreement formulation, the Intrinsic TE (ITE) aims to discount such synergistic
information to quantify the degree to which $X$ is \emph{individually}
predictive of $Y$, independent of $Y$'s past. In this paper, an estimator of
the ITE is proposed that is inspired by the recently proposed Mutual
Information Neural Estimation (MINE). The estimator is based on variational
bound on the KL divergence, two-sample neural network classifiers, and the
pathwise estimator of Monte Carlo gradients.
