Diagnosis is essential in modern chip production to increase yield, and debug constitutes a major part in the presilicon development process. For recent process technologies, defect mechanisms are increasingly complex, and continuous efforts are made to model these defects by using sophisticated fault models. Traditional static approaches for debug and diagnosis with a simplified fault model are more and more limited. In this paper, a method is presented, which identifies possible faulty regions in a combinational circiut, based on its input/output behavior and independent of a fault model. The new adaptive, statistical approach combines a flexible and powerful effect-cause pattern analysis algorithm with high-resolution ATPG. We show the effectiveness of the approach through experiments with benchmark and industrial circuits.