# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 5 Conclusions  
This paper proposes the IECI, a pipelined framework for event causality identification that integrates two modules. The PRED module achieves event detection through structural semantic fusion with a prompt-based mechanism and provides high-quality semantic input for subsequent causal recognition modeling. The SRCIG module builds and iteratively optimizes document-level event causal graphs through an iterative mechanism based on event detection results, fusing semantic role labeling, adaptive thresholding, and causal inference complementation to capture implicit causal relationships across sentences. Experimental results on the EventStoryLine and MAVEN-ERE datasets validate the superior performance of both modules and demonstrate the combined strengths of the IECI framework in terms of structural modeling, semantic understanding, and interpretability. However, while the model performs well within documents, its ability to generalize to cross-document scenarios with sparse or ambiguous contextual links remains limited. Future work can further explore the joint optimization mechanism of event detection and event causality \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Intra-Sentence (\%)**} & \multicolumn{4}{c}{**Inter-Sentence (\%)**} & \multicolumn{4}{c}{**Intra + Inter (\%)**} & \multicolumn{1}{c}{**Runtime**} \\ \cline{2-11}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **(min/fold)** \\ \hline IECI (Ours) & 71.7 & 50.2 & 64.9 & 63.7 & 48.3 & 56.5 & 66.0 & 48.1 & 56.2 & 326.6 \\ \(w\)/o SRL & 69.5 & 44.9 & 62.8 & 57.4 & 43.8 & 52.3 & 61.8 & 42.9 & 53.4 & 310.7 \\ \(w\)/o Dynamic Threshold & 70.2 & 48.7 & 63.4 & 62.4 & 45.1 & 53.6 & 63.9 & 46.3 & 54.3 & 319.5 \\ \(w\)/o SimFilter & 68.8 & 47.1 & 63.6 & 59.1 & 47.2 & 55.1 & 61.9 & 43.8 & 54.5 & 304.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment 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{4}{c}{**Intra-Sentence (\%)**} & \multicolumn{4}{c}{**Inter-Sentence (\%)**} & \multicolumn{4}{c}{**Intra + Inter (\%)**} & \multirow{2}{*}{**Runtime**} \\ \cline{2-2} \cline{4-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline IECI (Ours) & 76.5 & 67.5 & 70.8 & 54.8 & 65.4 & 58.5 & 59.5 & 64.0 & 60.7 & 175.8 \\ \(w\)/o SRL & 74.7 & 62.3 & 69.2 & 49.7 & 62.1 & 54.9 & 55.6 & 60.2 & 57.1 & 170.6 \\ \(w\)/o Dynamic Threshold & 75.4 & 65.6 & 70.1 & 53.5 & 62.9 & 56.1 & 57.3 & 61.4 & 58.0 & 171.2 \\ \(w\)/o SimFilter & 74.4 & 63.7 & 68.6 & 50.1 & 64.5 & 56.7 & 56.3 & 60.5 & 57.9 & 167.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment results of IECI on EventStoryLine. identification, as well as the generalization ability of cross-document causal inference to provide stronger support for causal inference in complex semantic scenarios. Conceptualization, Y.C. ; software, H.C., Z.S., Y.Z. and H.Z. ; validation, H.C., Z.S., Y.Z. and H.Z. ; resources, Y.C. ; data curation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, H.C. and Y.C. ; supervision, Y.C. ; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Technology R&D Program of China, grant number 2019 YFC1606401, the National Natural Science Foundation of China, grant number 72301010, the Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions, grant number BPHR2020203061, the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions, grant number BPHR20220104, and the Beijing Scholars Program, grant number 099. Not applicable.