  Falsification is drawing attention in quality assurance of heterogeneous
systems whose complexities are beyond most verification techniques'
scalability. In this paper we introduce the idea of causality aid in
falsification: by providing a falsification solver -- that relies on stochastic
optimization of a certain cost function -- with suitable causal information
expressed by a Bayesian network, search for a falsifying input value can be
efficient. Our experiment results show the idea's viability.
