# 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
This demonstrates that IECI maintains stable and efficient performance when dealing with mixed types of event relationships. We further count the runtime of each model to measure the computational efficiency of the model while ensuring performance. From the results, on the EventStoryLine dataset, traditional methods, such as LIP and BERT, have shorter runtimes due to their simpler structure and short running time of 100.4 min and 117.8 min, respectively. They perform poorly in causal relation recognition performance. The running time of RichGCN and ERGO is at a moderate level, which is 141.8 min and 139.5 min, respectively. The running time of PPAT and HOTECI is relatively high, at 156.6 min and 167.0 min, respectively. On the MAVEN-ERE dataset, BERT, ERGO, and PPAT show similar trends. Overall, our proposed IECI has achieved higher performance in exchange for a certain amount of time. **Case Study. ** We further validate the effectiveness of the IECI model in identifying causal relationships between events through case studies. As shown in Figure 3, BERT demonstrates the ability to identify causal event pairs within a single sentence. But, it still \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Intra-Sentence(\%)**} & \multicolumn{3}{c}{**Inter-Sentence(\%)**} & \multicolumn{3}{c}{**Intra + Inter (\%)**} & \multirow{2}{*}{**Runtime**} \\ \cline{2-2} \cline{6-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **(min/fold)** \\ \hline BERT [41] & 43.7 & 5.9 & 10.5 & 29.4 & 12.3 & 17.4 & 30.8 & 10.7 & 15.9 & 217.5 \\ ERGO [30] & 63.1 & 65.3 & 64.2 & 48.7 & 62.0 & 54.6 & 49.6 & 62.3 & 55.2 & 256.1 \\ PPAT [31] & 37.9 & 66.7 & 47.7 & 28.2 & 40.8 & 33.6 & 31.3 & 45.1 & 37.0 & 289.7 \\ IECI (Ours) & 71.7 & 50.2 & 64.9 & 63.7 & 48.3 & 56.5 & 66.0 & 48.1 & 56.2 & 326.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experimental results of IECI on MAVEN-ERE. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Intra-Sentence (\%)**} & \multicolumn{3}{c}{**Inter-Sentence (\%)**} & \multicolumn{3}{c}{**Intra + Inter (\%)**} & \multirow{2}{*}{**Runtime**} \\ \cline{2-2} \cline{6-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **(min/fold)** \\ \hline LIP [28] & 38.8 & 52.4 & 44.6 & 35.1 & 48.2 & 40.6 & 36.2 & 49.5 & 41.9 & 100.4 \\ BERT [41] & 47.8 & 57.2 & 52.1 & 36.8 & 29.2 & 32.6 & 41.3 & 38.3 & 39.7 & 117.8 \\ RichGCN [29] & 49.2 & 63.0 & 55.2 & 39.2 & 45.7 & 42.2 & 42.6 & 51.3 & 46.6 & 141.8 \\ ERGO [30] & 49.7 & 72.6 & 59.0 & 43.2 & 48.8 & 45.8 & 46.3 & 50.1 & 48.1 & 139.5 \\ PPAT [31] & 62.1 & 68.8 & 65.3 & 54.0 & 50.2 & 52.0 & 56.8 & 56.0 & 56.4 & 156.6 \\ HOTECI [42] & 66.1 & 72.3 & 69.1 & 81.4 & 40.6 & 55.1 & 63.1 & 51.2 & 56.5 & 167.0 \\ IECI (Ours) & 76.5 & 67.5 & 70.8 & 54.8 & 65.4 & 58.5 & 59.5 & 64.0 & 60.7 & 175.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results of IECI on EventStoryLine. exhibits limitations in handling implicit causal relationships, cross-sentence reasoning, and other aspects. In contrast, IECI correctly identifies causal relationships between events by constructing a global inference chain. (1) IECI can identify succession-based causal relationships. For example, the \"pulling off\" in an event pair (transfer, pulling off) occurs during the \"transfer\" process, which constitutes a causal relationship. (2) IECI can identify causal relationships in indirect reasoning. For example, in the event pair (pulling off, received), there is an \"escape\" step between \"pulling off\" and \"received\", and the model can avoid interference and make a correct identification. (3) IECI can identify causal chains of multiple events correctly. For example, the event pairs (transfer, pulling off) and (pulling off, received) are connected through the intermediate event \"pulling off\".