  The probability distribution of number of ties of an individual in a social
network follows a scale-free power-law. However, how this distribution arises
has not been conclusively demonstrated in direct analyses of people's actions
in social networks. Here, we perform a causal inference analysis and find an
underlying cause for this phenomenon. Our analysis indicates that heavy-tailed
degree distribution is causally determined by similarly skewed distribution of
human activity. Specifically, the degree of an individual is entirely random -
following a "maximum entropy attachment" model - except for its mean value
which depends deterministically on the volume of the users' activity. This
relation cannot be explained by interactive models, like preferential
attachment, since the observed actions are not likely to be caused by
interactions with other people.
