  We describe a method to perform functional operations on probability
distributions of random variables. The method uses reproducing kernel Hilbert
space representations of probability distributions, and it is applicable to all
operations which can be applied to points drawn from the respective
distributions. We refer to our approach as {\em kernel probabilistic
programming}. We illustrate it on synthetic data, and show how it can be used
for nonparametric structural equation models, with an application to causal
inference.
