Enhancing Event Causality Identification with Counterfactual Reasoning (2023)

Abstract: Existing methods for event causality identification (ECI) focus on mining potential causal signals, i.e., causal context keywords and event pairs. However, causal signals are ambiguous, which may lead to the context-keywords bias and the event-pairs bias. To solve this issue, we propose the counterfactual reasoning that explicitly estimates the influence of context keywords and event pairs in training, so that we are able to eliminate the biases in inference.Experiments are conducted on two datasets, the result demonstrates the effectiveness of our method.

## introduction event causality identification (eci) aims to identify causal relations between event pairs. for example, given the sentence "the earthquake generated a tsunami.", an eci system should identify that a causal relation holds between the two mentioned events, i.e., earthquake cause -→ tsunami. a good eci system is able to discover a large number of causal relations from text and hence supports lots of intelligence applications, such as commonsense causal reasoning @xcite , narrative story generation @xcite , and many others. existing methods focus on mining potential causal signals, including causal context keywords @xcite and causal event pairs @xcite @xcite , to enhance eci. by mining potential causal signals, these methods improve the coverage of unseen events and causal relations, which is the reason for their success. however, they face the risk of amplifying the role of potential signals, resulting in biased inference. due to the polysemy of language, causal signals are ambiguous. the occurrence of those signals does not always indicate that causality is established. that is, ambiguous context keywords and event pairs may lead to the context-keywords bias and the event-pairs bias in eci. specifically, in sentence label a 6.1-magnitude earthquake which hit the indonesian province of aceh on tuesday killed at least one person, injured dozens and destroyed buildings, sparking panic in a region devastated by the quake-triggered tsunami of 2004. 0 to solve this issue, it is intuitively to explicitly estimate the influence of context keywords and event pairs in training, so that we can mitigate those biases in inference. motivated by this idea and existing dataset-debiasing works @xcite @xcite , we introduce factual and counterfactual reasoning for eci. the factual reasoning takes the entire samples as input, which captures the combined features between context keywords and the event pairs, with the sideeffect of learning features of biases. the counterfactual reasoning considers the two situations where only context keywords or event pairs are available. intuitively, in counterfactual reasoning, a model can only make predictions based on context keywords or event pairs, so that the biases can be identified. in inference, we use counterfactual reasoning to estimate context-keywords bias and eventpairs bias, then subtract the biases from the factual predictions. to achieve this goal, we must locate the exact position of context keywords in a sen-tence foot_0 . but this is difficult because it requires extensive manual annotation. to avoid this, we adopt a model-based strategy. considering the powerful feature extraction ability of pre-trained language models (plms), if we feed an event-removed sentence into plms, plms should be able to pay the most attention to the important context keywords. based on this assumption, we split a sentence into two exclusive parts: an event-masked context and an event pair. they are fed into the counterfactual reasoning module to learn the context-keywords bias and event-pairs bias. to summarize, we consider the spurious correlation problem in eci, which may make an eci model overfit on ambiguous causal signals. to mitigate this problem, we propose a counterfactual reasoning mechanism for eci. to the best of our knowledge, this is the first work that studies eci from a counterfactual perspective. we conduct extensive experiments on two benchmark datasets. the result shows that our method is effective. ## counterfactual eci previous eci methods may overfit the ambiguous context keywords and event pairs, making biased inferences. we use counterfactual reasoning to eliminate this issue. our method is depicted in figure 1 , which consists of a factual reasoning module and a counterfactual reasoning module. ## related work event causality identification there are mainly two types of eci works: document-level eci @xcite and sentencelevel eci. in this work, we pay attention to the sentence-level eci. @xcite propose to mask event mentions to mine event-agnostic causal patterns. @xcite devises self-supervised methods to learn context-specific causal patterns from external causal statements. @xcite utilize causal event pairs to find useful data from external resources. nevertheless, these methods rely on ambiguous causal signals, and may learn the spurious correlations between ambiguous causal signals and labels. different from these works, we regard eci from a counterfactual perspective, and devise a counterfactual inference module to the spurious correlations in eci. counterfactual reasoning counterfactual data augmentation is a data-level manipulation, which is effective to mitigate biases in datasets @xcite . however, it needs extra manual cost of data annotation. a recent trend is counterfactual reasoning, which imagines the situation that what will the prediction be if seeing only the biased part in the input. in this way, the biases can be distilled and eliminated in the inference. this strategy avoids data annotation, and is adopted by many works @xcite @xcite . motivated by these works, we devise the counterfactual reasoning module to make a de-baised eci inference. ## conclusion we discuss the issue of context-keywords and eventpairs biases in eci. to mitigate this problem, we propose the counterfactual reasoning which explicitly estimates the influence of the biases, so that we can make a de-biased inference. experimental results demonstrate the significant superiority of our method. the robustness and explainability of our method are also verified by further studies.