With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction. Yet, existing association-based approaches on rationalization cannot identify true rationales when two or more snippets are highly inter-correlated and thus provide a similar contribution to prediction accuracy, so-called spuriousness. To address this limitation, we novelly leverage two causal desiderata, non-spuriousness and efficiency, into rationalization from the causal inference perspective. We formally define a series of probabilities of causation based on a newly proposed structural causal model of rationalization, with its theoretical identification established as the main component of learning necessary and sufficient rationales. The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets with extensive experiments compared to state-of-the-art methods.

翻译：随着自然语言处理的最新进展，合理化成为一种重要的自解释框架，通过选择输入文本的子集来解释预测中的主要变化，从而解开黑箱模型。然而，现有的基于关联的合理化方法在多个片段高度相关且对预测准确性提供类似贡献时（即所谓的虚假性），无法识别真正的因果依据。为解决这一局限性，我们从因果推断角度出发，创新性地将两个因果标准——非虚假性和高效性——引入合理化过程。我们基于新提出的合理化结构因果模型，正式定义了一系列因果概率，并将其理论可识别性确立为学习必要且充分因果依据的主要组成部分。通过在实际评论数据集和医学数据集上的广泛实验，与最先进方法相比，所提出的因果合理化方法展现了优越性能。