  This paper establishes the existence of observable footprints that reveal the
"causal dispositions" of the object categories appearing in collections of
images. We achieve this goal in two steps. First, we take a learning approach
to observational causal discovery, and build a classifier that achieves
state-of-the-art performance on finding the causal direction between pairs of
random variables, given samples from their joint distribution. Second, we use
our causal direction classifier to effectively distinguish between features of
objects and features of their contexts in collections of static images. Our
experiments demonstrate the existence of a relation between the direction of
causality and the difference between objects and their contexts, and by the
same token, the existence of observable signals that reveal the causal
dispositions of objects.
