Sentiment classification is a crucial task in natural language processing (NLP). To mitigate the spurious correlation, the causal word identification method estimates the impact of treatment words on sentence sentiment and removes those with low treatment effects. However, previous works regard the presence or absence of a specific word in a sentence as a binary treatment. This approach limits the generalizability to novel words and the robustness of low-frequency words. To bridge this gap, we propose a meta-causal approach that achieves causal word identification for arbitrary words with a single training task. Specifically, we begin by clustering contexts based on their embeddings obtained from a pre-trained language model. Subsequently, for each cluster, a representation and multi-head prediction networks are trained to estimate the treatment effect of each word to distinguish causally related words from spuriously correlated ones. The trained word classifier is then used to give weights for different words to train a more robust and generalizable sentiment classification model. Extensive experiments on public datasets demonstrate the effectiveness of our method in identifying causal words and improving the performance of sentiment classification.

Identifying causal relationships rather than spurious correlations between words and class labels plays a crucial role in building robust text classifiers. Previous studies proposed using causal effects to distinguish words that are causally related to the sentiment, and then building robust text classifiers using words with high causal effects. However, we find that when a sentence has multiple causally related words simultaneously, the magnitude of causal effects will be significantly reduced, which limits the applicability of previous causal effect-based methods in distinguishing causally related words from spuriously correlated ones. To fill this gap, in this paper, we introduce both the probability of necessity (PN) and probability of sufficiency (PS), aiming to answer the counterfactual question that ‘if a sentence has a certain sentiment in the presence/absence of a word, would the sentiment change in the absence/presence of that word?’. Specifically, we first derive the identifiability of PN and PS under different sentiment monotonicities, and calibrate the estimation of PN and PS via the estimated average treatment effect. Finally, the robust text classifier is built by identifying the words with larger PN and PS as causally related words, and other words as spuriously correlated words, based on a contrastive learning approach name CPNS is proposed to achieve robust sentiment classification. Extensive experiments are conducted on public datasets to validate the effectiveness of our method.