Interventional Rationalization

Selective rationalizations improve the explainability of neural networks by selecting a subsequence of the input (i.e., rationales) to explain the prediction results. Although existing methods have achieved promising results, they still suffer from adopting the spurious correlations in data (aka., shortcuts) to compose rationales and make predictions. Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales. Specifically, we first analyse the causalities among the input, rationales and results with a causal graph. Then, we discover spurious correlations between the input and rationales, and between rationales and results, respectively, by identifying the confounder in the causalities. Next, based on the backdoor adjustment, we propose a causal intervention method to remove the spurious correlations between input and rationales. Further, we discuss reasons why spurious correlations between the selected rationales and results exist by analysing the limitations of the sparsity constraint in the rationalization, and employ the causal intervention method to remove these correlations. Extensive experimental results on three realworld datasets clearly validate the effectiveness of our proposed method. The source code of Inter-RAT is available at https://github. com/yuelinan/Codes-of-Inter-RAT . * Corresponding Author selector predictor Manslaughter The defendant and the victim fought over a trivial matter the defendant punched the victim in the face with the fist, causing the victim to fall and hit his head on the ground resulting in serious injuries. The defendant immediately resuscitated the victim but he died after being sent to hospital...... The defendant and the victim fought over a trivial matter, the defendant punched the victim in the face with the fist, causing the victim to fall and hit his head on the ground resulting in serious injuries. The defendant immediately resuscitated the victim but he died after being sent to hospital......