  Motivated by the detection of cascades of defaults in economy, we developed a
detection framework for an endogenous spreading based on causal motifs we
define in this paper. We assume that the change of state of a vertex can be
triggered by an endogenous or an exogenous event, that the underlying network
is directed and that times when vertices changed their states are available. In
addition to the data of company defaults, we also simulate cascades driven by
different stochastic processes on different synthetic networks. We show that
some of the smallest motifs can robustly detect endogenous spreading events.
Finally, we apply the method to the data of defaults of Croatian companies and
observe the time window in which an endogenous cascade was likely happening.
