Smooth process execution relies on high-quality insights extracted from event data. For instance, trace durations heavily affect performance and increase resource consumption. While many predictive systems aim to identify these inefficiencies, they often focus on individual process instances, missing the global perspective. It is essential to detect where delays occur globally and pinpoint specific activity transitions causing them. To address this, we propose CC-HIT (Creating Counterfactuals from High-Impact Transitions), which identifies temporal activity dependencies across the process. CC-HIT uses a modified game theoretic approach and counterfactual information to generate reference event logs to estimate the consequences of activity transitions. It highlights key activity transitions impacting process performance, offering actionable insights for optimization. Validation on the BPIC 2020 dataset demonstrates its effectiveness over baseline methods.