## 2 Related Work
### Causal Information Extraction Methods  
The extraction of causal information from documents is a topic of great interest since not only numerical data but also texts can provide valuable insight into causal relations [24]. While causal discovery aims to uncover the causal model or, at the very least, a Markov equivalent that mimics the data generation process for a given data set [25], causal information extraction from texts focuses on identifying causal entities and how they are connected to each other [7]. The current existing methods for causal information extraction are broadly categorized by the authors in [7] into three main categories: (i) Pattern-based approaches; (ii) Statistical machine learning-based approaches; (iii) Deep learning approaches. Pattern-based approaches usually leverage linguistic patterns to identify causal language in limited contexts. For instance, the authors of ref. [6] use lexical and syntactic patterns to detect causal relations in medical and business texts. Pattern-based approaches can be effective for specific domains but they lack generalizability. Statistical machine learning-based approaches typically involve the use of third-party NLP tools which require fewer patterns than pattern-based approaches [26]. Deep learning approaches leverage deep neural networks to extract causal information from text [27] and to acquire useful vectorized representations of text [8; 9; 10]. This involves training neural networks to identify causal relationships in text using techniques such as supervised learning. As such, many scholars have been working on creating annotation guidelines [28; 29; 30] and annotating data sets to train and test the developed methods [22; 31]. One of the most common machine learning and deep learning approaches for extracting causal information is the use of sequence tagging. Causal information extraction is not only about identifying causes and effects but about detecting them as pairs. To obtain useful causal information, the relation between the entities needs to be taken into consideration [7]. Pure sequence tagging methods, such as the method presented in [10; 32], are facing difficulties in integrating relational information into the input data while performing Named Entity Recognition (NER). Since the relation between the entities is crucial in order to obtain useful causal knowledge, other approaches have been proposed which involve fine-tuning pre-trained language models for text span classification and sequence labeling tasks. For example, the authors in [10] specifically label the text spans corresponding to cause and effect in a given text. They then proceed to classify whether these identified cause-and-effect spans were linked together through a causal relation. Similarly, [32] employ an event-aware language model in order to predict causal relations by taking into account event information, sentence context, and masked event context. Another major difficulty in the extraction of causality using NER is the recognition of overlapping and nested entities. [33] tackle overlapping entities by employing Text-to-Text Transfer Transformer (T5). Nevertheless, profound nested relations in which whole causal relations are nested within an entity are not captured with their method. Also, [PERSON] [8] proposed an MST approach for extracting causal information from historic texts. The MST method extracts causal cues in the first stage and then uses this information to extract complete causal relations in subsequent stages. In summary, causal information extraction from text depends on several success factors, which include the following:  
* Efficient generalizability achieving broad applicability of the method with limited manual work, such as creating new knowledge bases for domain-specific patterns. * The ability to acquire useful vectorized representations of the text that enables the training of different methods for causal information extraction.