# 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  
**Comparison Experiments. ** To validate the effectiveness of IECI for event causality identification, we conduct comparative experiments on the EventStoryLine dataset and the MAVEN-ERE dataset. Specifically, IECI is compared with several state-of-the-art models on EventStoryLine, including LIP [28], BERT [41], RichGCN [29], ERGO [30], PPAT [31], and HOTECI [42]. Furthermore, IECI is compared with several state-of-the-art models on MAVEN-ERE, including BERT [41], ERGO [30], and PPAT [31]. The comparison is carried out under three different settings: intra-sentence, inter-sentence, and combined intra-sentence and inter-sentence scenarios. The experimental results under these settings are presented in Tables 3 and 4. \"Runtime (min/fold)\" denotes the average time for training and evaluation during five-fold cross-validation. In the intra-sentence, IECI achieves superior performance on both datasets. On the EventStoryLine dataset, IECI achieves an F1 score of 70.8%, representing a 1.7% improvement over the 69.1% achieved by the currently better-performing HOTECI model. And, IECI maintains high precision of 76.5% and recall of 67.5%, demonstrating strong capabilities in fine-grained semantic modeling. On the more challenging MAVEN-ERE dataset, IECI still performs well, achieving an F1 score of 64.9%. Compared with BERT, ERGO, and PPAT, the F1 score of IECI increases by 54.4%, 0.7%, and 20.2%, respectively, which indicates that the IECI has stronger causal identification ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Event Detection (\%)**} & **Runtime** \\ \cline{2-5}  & **P** & **R** & **F1** & **(min)** \\ \hline BERT-NER [38] & 86.4 & 84.9 & 85.6 & 31.5 \\ BERT-CRF-NER [39] & 89.1 & 87.7 & 88.2 & 38.6 \\ BERT-BiLSTM-NER [40] & 90.1 & 89.9 & 91.7 & 50.2 \\ PRED (Ours) & 95.9 & 95.2 & 95.4 & 73.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative experimental results of PRED on EventStoryLine. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Event Detection (\%)**} & **Runtime** \\ \cline{2-5}  & **P** & **R** & **F1** & **(min)** \\ \hline BERT-NER [38] & 86.1 & 83.0 & 86.1 & 24.2 \\ BERT-CRF-NER [39] & 89.7 & 86.1 & 89.3 & 30.0 \\ BERT-BiLSTM-NER [40] & 91.4 & 89.5 & 92.2 & 36.3 \\ PRED (Ours) & 96.5 & 96.2 & 96.4 & 56.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experimental results of PRED on MAVEN-ERE. In the inter-sentence, IECI also shows strong inference. This task is more difficult, requiring the model to cross sentences, integrate contextual information, and capture potential semantic prompts. On the EventStoryLine dataset, despite ranking second in accuracy among all models, IECI achieves an F1 score of 58.5%, the highest among all models. Compared to HOTECI's 55.1% and PPAT's 52.0%, IECI demonstrates stronger cross-sentence reasoning and context integration. On MAVEN-ERE, IECI still leads with an F1 score of 56.5%, showing a significant improvement compared to BERT and PPAT. The advantage is mainly due to the model's causal graph structure and iterative update mechanism. This mechanism dynamically adjusts the threshold based on event density. Ultimately, the updating and expansion of causal edges are effectively controlled, thus enhancing the generalization ability and robustness of the model in complex contexts. In the overall causal identification scenario (Intra + Inter), IECI also achieves leading comprehensive performance. On the EventStoryLine dataset, precision, recall, and F1 score reach 59.5%, 64.0%, and 60.7%, respectively. The F1 score improves by 4.2% over HOTECI, the best of the baseline models, and by 18.8% over LIP, the lowest. In MAVEN-ERE, IECI still achieves a 56.2% F1 score, which represents an improvement of 1.0% compared to the best baseline model, ERGO, and 40.3% compared to the lowest-performing model, BERT.