Interventional Rationalization (2023)

Abstract: 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 structural causal model. 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 real-world 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.

## introduction the remarkable success of deep neural networks (dnns) in natural language processing tasks has prompted the interest in how to explain the results of dnns. among them, the selective rationalization task @xcite @xcite has received increasing attention, answering the question "what feature has a significant impact on the prediction results of the model?". specifically, the goal of rationalization is to extract a small * corresponding author a 6 e a i s x i 6 j z t k q q u y j u b x 6 o s 5 b a r j 3 g b 4 j f h t y l h f t a v j l g 1 y 9 4 6 k v 6 m m i u r 9 5 7 o t f e u b 0 k d t n + o c x z u t k q 2 v b k p t g v l p t 3 q l n a w j i l n c w d l h k c c k n l f 4 h f p e d y o j s v j x r j 9 t d u y w r o k b 8 t 4 + a b k s z d / < / l a t e x i @xmath0 to solve that, @xcite propose an environment-invariant method (invrat) to discover the causal rationales. they argue that the causal rationales should remain stable as the environment shifts, while the spurious correlation between input and labels vary. although this method performs well in selecting rationales, since the environment in rationalization is hard to observe and obtain, we argue that this "causal pattern" can be further explored to improve the rationalization. along this research line, in this paper, we propose an interventional rationalization (inter-rat) method which removes the spurious correlation by the causal intervention @xcite . specifically, motivated by the causal inference theory, we first formulate the causal relationships among x, r and y in a causal graph @xcite as shown in figure 2(a). then, we identify the confounder c in this causal graph, which opens two backdoor paths x ← @xmath1, making x and r, r and y spuriously correlated. next, we address the above correlations, respectively. for spurious correlations between x and r, we assume the confounder is observed and intervene the x (i.e., calculating p (r|do(x)) instead of p (r|x)) to block the backdoor path and remove the spurious correlations based on the backdoor adjustment @xcite . among them, the do-operation denotes the pursuit of real causality from x to r. for spurious correlations in r and y , since by the definition of r (rationales are the only basis for yields prediction results), we argue that there should be no spurious correlations between r and y . however, in practice, we discover the sparsity constraint commonly defined in rationalization @xcite @xcite , ensuring the selector to extract short rationales, results in the spurious correlations between r and y . therefore, we further analyse this discovery and employ the causal intervention to remove these correlations. our experiments are conducted on three real-world datasets, and the experimental results validate the effectiveness of removing spurious correlation with causal interventions. ## framework of rationalization this section formally defines the problem of rationalization, and then presents the details about the conventional rationalization framework consisting of the selector and predictor, where these two components are trained cooperatively to generate rationales and yield the prediction results. ## interventional rationalization in this section, we first reveal how the confounder c causes spurious correlations in rationalization with a causal graph. then, we remove these correlations by using a causal intervention method. ## experiments in this section, we validate the effectiveness of our method on three real-world tasks including the beer reviews sentiment analysis, movies reviews prediction and the legal judgment prediction. ## conclusion in this paper, we proposed a causal intervention method (inter-rat) to improve rationalization. to be specific, we first formulated the causalities in rationalization with a causal graph and revealed how the confounder hurt the performance of selecting rationales with opened backdoor paths. then, we introduced a backdoor adjustment method to remove spurious correlations between inputs and rationales. besides, we further discussed the potential bias between selected rationales and predicted results caused by the sparsity constraints, and adopted the above causal intervention method to yield de-confounded prediction results. experimental results on real-world datasets have clearly demonstrated the effectiveness of inter-rat.