# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 4 Experiments
### Experimental Results and Discussion
#### 4.3.2 Experiments for IECI
IECI is able to form a complete inference path (transfer, pulling off, received) and identifies that (transfer, received) has a causal relationship. (4) IECI can correctly identify long-distance causal relationships across sentences. For example, the event pair (run, received) is located far apart in different sentences and has actions like \"pulling off\", \"transfer\", and \"escape\" between. IECI can still capture the deep semantic connections between events. At the same time, it can also recognize that there is no causal relationship in (run, transfer). **Ablation Experiments. ** To verify the effectiveness of the key modules in the IECI proposed in this paper, we designed three ablation experiments to remove the semantic role labeling, the dynamic threshold adjustment mechanism, and the semantic similarity judgment mechanism (SimMatch), respectively, and analyze their impacts on the performance of ECL. The ablation experiment results on two datasets are presented in Tables 5 and 6. The model's performance drops significantly when the SRL module is unused. Specifically, on the EventStoryLine dataset, the F1 score for intra-sentence causal recognition decreases from 70.8% to 69.2%, and the F1 score for inter-sentence causal recognition decreases from 58.5% to 54.9%. On the MAVEN-ERE dataset, the F1 scores of intra-sentence and inter-sentence decrease by 2.1% and 4.2%, respectively. This indicates that semantic role information plays a crucial role in modeling the semantic structure of events and improving the accuracy of causality recognition, especially when dealing with sentences with complex structures or semantic ambiguity in documents. We remove the dynamic threshold adjustment mechanism and uniformly use a fixed threshold (e.g., \(\theta\) = 0.6) for relationship discrimination for all samples. On the EventStoryLine dataset, the overall F1 score of the model decreases from 60.7% to 58.0%, a 2.7% drop. The F1 scores of intra-sentence and inter-sentence decreases by 0.7% and 2.4%, respectively. On the MAVEN-ERE dataset, the overall F1 score of the model also decreases by 1.9%. This indicates that the dynamic threshold adjustment mechanism makes a significant  
Figure 3: A case study. contribution to improving overall performance. Further analysis of intra-sentence and inter-sentence scenarios shows a notable decline in performance, mainly due to the inability of fixed thresholds to effectively capture low-confidence but true causal relationships in event-dense long texts. Without using the similarity judgment mechanism, the model introduces more noisy edges in the causal graph inference complementation phase, which leads to a drop in performance for intra-sentence, inter-sentence, and overall causal identification on the EventStoryLine and MAVEN-ERE datasets. This suggests that the semantic similarity judgment mechanism helps prevent both false connections and missed connections in the inference process and plays an important role in improving the robustness of ECI. On both datasets, the runtime analysis shows that there is a trade-off between overall model performance and runtime efficiency. The full model of IECI has a relatively high runtime while maintaining optimal recognition performance. This suggests that the added components, while increasing the computational cost, result in significant performance gains and maintain good scalability across documents of varying lengths and event densities. The increased runtime of IECI is primarily attributed to the similarity calculations performed between events.