# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 1 Introduction  
Event Causality Identification (ECI) plays a vital role in the field of Natural Language Processing (NLP), which helps to both deeply understand the potential relationships between events in text and supports practical applications, such as Intelligent Recommendation [1] and Intelligent Education [2]. However, event causality often spans multiple sentences or even paragraphs in real corpus, with strong long-range dependency. These cross-sentence causal relations are difficult to capture through local windows or static contexts, further increasing the challenges of modeling [3]. Traditional methods, limited to surface or local context features, struggle to capture deeper semantic or causal structures. In recent years, some studies have attempted to introduce Graph Neural Networks (GNNs) to construct causal graphs between events [4] and to infer causal relations between events through graph structures. However, these methods still face challenges, such as inadequate control of information propagation in causal graphs, insensitivity to edge weight updates, and introduction of potentially redundant edges. These can easily lead to the over-propagation of erroneous causal paths, affecting the accuracy of identification and theinterpretability of causal chains. Recently, there have been studies exploring the use of sequence decoders to directly generate causal event pairs or causal chains. These approaches use pre-trained Encoder Decoder models (such as T5, GPT) to enhance the flexibility and generation capability of document-level causal inference [5; 6]. Event Detection (ED) aims to identify the presence of predefined events in a text and locate the corresponding event-related content. Most traditional approaches treat event detection as a classification or sequence labeling problem. However, events are expressed in various forms, with fuzzy semantic boundaries, and some do not even have obvious clues. As a result, models tend to rely on a lot of fine-grained annotations, increasing the annotation cost [7]. In recent years, Pretrained Language Models (PLMs) have performed well in event detection tasks [8]. These models can capture contextual semantic associations by being pre-trained on large-scale corpora. They also have good migration capabilities and can even be applied to weakly supervised and unsupervised scenarios [9]. In addition, natural language prompting approaches have been gradually introduced into event detection, transforming the original structured task into a language understanding problem and providing a new perspective on event detection. Although these methods have led to notable advancements, their application to event causality reasoning remains limited. To address the challenges above, this paper proposes a pipeline framework for iterative event causality identification with dynamic inference chains (IECI). IECI consists of two modules, where ED is both an independent detection task and a critical step providing ECI input. Specifically, the Prompt-Based Event Detection (PRED) module effectively extracts events from the raw text by integrating semantic understanding with prompt-based reasoning. These events provide clear inputs for causal recognition. On this basis, the Semantic-Role Guided Causal Inference Graph (SRCIG) module performs iterative reasoning to discover new causal relations. It also dynamically optimizes the causal graph structure, enhancing the overall accuracy and robustness of causal inference. The experimental results show that the proposed IECI framework performs strongly in both modules, demonstrating a wide range of adaptability and application potential. The main contributions of this paper are as follows:  
1. We propose a pipeline-based event causality identification framework, IECI, in which the SRCIG module performs causal inference by incorporating semantic role guidance, dynamic threshold control, and iterative optimization mechanisms. 2.