  Causal inference from observation data is a core problem in many scientific
fields. Here we present a general supervised deep learning framework that
infers causal interactions by transforming the input vectors to an image-like
representation for every pair of inputs. Given a training dataset we first
construct a normalized empirical probability density distribution (NEPDF)
matrix. We then train a convolutional neural network (CNN) on NEPDFs for
causality predictions. We tested the method on several different simulated and
real world data and compared it to prior methods for causal inference. As we
show, the method is general, can efficiently handle very large datasets and
improves upon prior methods.

Machine Learning;Machine Learning