  One of the basic aims in science is to unravel the chain of cause and effect
of particular systems. Especially for large systems this can be a daunting
task. Detailed interventional and randomized data sampling approaches can be
used to resolve the causality question, but for many systems such interventions
are impossible or too costly to obtain. Recently, Maathuis et al. (2010),
following ideas from Spirtes et al. (2000), introduced a framework to estimate
causal effects in large scale Gaussian systems. By describing the causal
network as a directed acyclic graph it is a possible to estimate a class of
Markov equivalent systems that describe the underlying causal interactions
consistently, even for non-Gaussian systems. In these systems, causal effects
stop being linear and cannot be described any more by a single coefficient. In
this paper, we derive the general functional form of causal effect in a large
subclass of non-Gaussian distributions, called the non- paranormal. We also
derive a convenient approximation, which can be used effectively in estimation.
We apply the method to an observational gene expression dataset.
