Event correlation is the task of detecting dependencies between events in event sequences, e.g., for predictive maintenance based on log-files. In this work, a new data-driven, generic framework for event correlation is presented. First, we use a fast preliminary test statistic to determine candidate event type pairs. Next, the precise distribution of the time lag between those pairs is calculated. For this purpose, a new efficient iterative method is developed that aligns two event sequences and finds the optimal event assignments. In our experiments, the proposed method is orders of magnitude faster than state-of-the-art methods but always yields similar (or even better) results.