  Recent formal approaches towards causality have made the concept ready for
incorporation into the technical world. However, causality reasoning is
computationally hard; and no general algorithmic approach exists that
efficiently infers the causes for effects. Thus, checking causality in the
context of complex, multi-agent, and distributed socio-technical systems is a
significant challenge. Therefore, we conceptualize an intelligent and novel
algorithmic approach towards checking causality in acyclic causal models with
binary variables, utilizing the optimization power in the solvers of the
Boolean Satisfiability Problem (SAT). We present two SAT encodings, and an
empirical evaluation of their efficiency and scalability. We show that
causality is computed efficiently in less than 5 seconds for models that
consist of more than 4000 variables.
