D-Separation for Causal Self-Explanation
Authors: Wei Liu
Jun Wang
Haozhao Wang
Ruixuan Li
Zhiying Deng
YuanKai Zhang
Yang Qiu
Venue: NeurIPS 2023 poster (2023)
Keywords: interpretability
causal inference
rationalization
self-explaining
TL;DR: A simple but effective approach for identifying causal rationales in self-explaining rationalization.

Abstract: Rationalization aims to strengthen the interpretability of NLP models by extracting a subset of human-intelligible pieces of their inputting texts. Conventional works generally employ the maximum mutual information (MMI) criterion to find the rationale that is most indicative of the target label. However, this criterion can be influenced by spurious features that correlate with the causal rationale or the target label. Instead of attempting to rectify the issues of the MMI criterion, we propose a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are \emph{d-separated} by the causal rationale. By minimizing the dependence between the non-selected parts of the input and the target label conditioned on the selected rationale candidate, all the causes of the label are compelled to be selected. In this study, we employ a simple and practical measure for dependence, specifically the KL-divergence, to validate our proposed MCD criterion.  Empirically, we demonstrate that MCD improves the F1 score by up to 13.7% compared to previous state-of-the-art MMI-based methods.
Our code is in an anonymous repository: https://anonymous.4open.science/r/MCD-CE88.

D-Separation for Causal Self-Explanation
Wei Liu' Jun Wang?'
Haozhao Wang
Ruixuan Li
Zhiying Deng' Yuankai Zhang' Yang Qiu'
"Scoool of Computer Science and Technology. Huazhong University of Science and Technology FiWddo Tech
[idc__w, bz_ hz_wang, rxli, dengzhiyingdd, maankai_zhang anders) @hust.eduu edu, cn "jang0inudao. tech
Abstract
Rationalization is a self-explaining framework for NLP models. Conventional work typically uses the maximum mutual information (MMI) criterion to find the rationale that is most indicative of the target label. However, this criterion can be influenced by spurious features that correlate with the causal rationale or the target label. Instead of attempting to rectify the issues of the MMI criterion, we propose novel criterion uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are d-separated by the causal rationale. By minimizing the dependence between the unselected parts of the input and the target label conditioned on the selected rationale candidate, all the causes of the label are compelled to be selected. In this study, we employ a simple and practical measure of dependence, specifically the KL-divergence,, to validate our proposed MCD criterion. Empirically, we demonstrate that MCD improves the F1 score by up 13.7% 7% compared to previous state-of-the-an MMI-based methods. Our code is http:////.gg..ooo//////////////a
available at: https:///gtthtii com http://gggggggg/aaaiaiaaaaaaaaaa
Introduction
X Input text
Explainer
Predictor
8
With the success of deep learning, there is growing concern about the inter- pretability of deep learn- ing models, particularly as they rapidly being de- ployed in various critical fields (Lipton 2018). Ide-
Rationale
Prediction
service is excyllent and very clean. good location being close tnail destirations in trwn.
service recrellent and vary closs www.-ggoolootiiionoooooooooo baiegcloon tas alll www.dddddittiissnnnnooo in Ieww..
[0.1. 0.
(service
The standard rationalization framework RNP. X is the orig- inal full text. Xz is the selected rationale candidate and ý is the predictor's 's output.
Figure 1:
ally, the explanation for prediction should be both faithful (reflecting the model's actual behavior) and plausible (aligning with human understanding) (Chan et al. 2022).
Post-hoc explanations, which are trained separately from the prediction process, may not faithfully represent agent's 's decision, despite appearing plausible (Lipton) 2018). Sometimes, faithfulness should be considered a prerequisite that precedes plausibility especially when these networks are employed to assist in critical decision processes, this factor determines the trustworthiness of the explanations. (or self-explaining) techniques typically offer increased transparency (Lipton) 2018) and faithfulness Yu et al., (021). as the prediction is made based on the explanation itself.
explanations neural networks,
contrast to post-hoc methods, ante-hoc
*Corresponding authors. This paper is a collaboration between Intelligent and Distributed Computing Laboratory, Huazhong University of Science and Technology Wuddo Tech
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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A
ante-hoc explanation framework, called Rationalizing Neural Predictions (RNP), was proposed by Lei al. (2016) and is also known as rationalization. RNP utilizes a cooperative game between explainer and a predictor, where the explainer identifies a numan-interpretalie subset of the input (referred to as rationale) and passes to the subsequent predictor for making predictions, as shown in Figure The explainer and predictor are trained cooperatively to maximize prediction accuracy. A significant advantage of RNN-based rationalization is its certification of exclusion, which guarantees that any unselected part of the input has no contribution to the prediction. This property ensures the maintenance of faithfulness, enabling us to focus solely on plausibility (Yu et al. 2021). Notably, although RNP was initially proposed in the field of NLP and and its enhancement schemes have primarily been validated using text data, its framework can also be applied to other domains, e.g., explainnng image classification (Yuan et al. 2022) and graph neural networks (Luo et al. 2020).
Previous rationalization methods generally utilize the maximum mutual information (MMJ) criterion to determine the rationale, defined as the subset most indicative of the target label. However, this criterion merely uncovers associations rather than causal relationships between the rationale and the label. Consequently, MMI is easily affected by spurious correlations and the plausibility of chosen rationales might be diminished, even though the rationales still faithfully report the predictor's behavior (Chang et al., 2020). In rationalization, there are two stages from which correlations may arise. The first type of correlation originates from the process of dataset generation, and we refer to it as feature correlation. A typical example of feature correlation, pointed out in LIME (Ribeiro) jet al. 2016), is that wolves often appear together with snow. Consequently, whether the background features snow or not can serve strong indicator for classifying an image as depicting wolf. Another instance of feature correlation is demonstrated in the first row of Table D Within a beer review, favorable taste often correlates with appealing aroma. Comments regarding the taste can serve as a strong indicator for the smell label. The predictor might inadvertently overfit to such correlations, leading to local optima. Consequently, the suboptimal predictor could mislead the explainer to select these spurious correlations. Another type of correlation stems from the rationale (mask) selection stage, and we call it mask correlation. An example of mask correlation is depicted in the second row of Table D Consider a situation where the explainer has implicitly learned the category of X, and selects 4_" for all negative inputs while excluding it from all positive inputs. In this case. the predictor only needs to determine whether the input rationale includes a "_" or not, Even though this phenomenon has not been analyzed from the perspective of spurious correlation, has been observed and named as degeneration in prior research (Yu et al. 2019)
Some methods have been developed to address either feature correlation degeneration separately. INVRAT (Chang et al. 2020) attempts to tackle feature correlation using invariant risk minimization (IRM) (Arjovsky et al.) 2019). The main idea is emphasize spurious (non-causal) variations by splitting the dataset into distinct environments. However, IRM--ased methods have several limitations. For instance, they require strong prior knowledge about the relationships between non-causal and causal features (c.g.. the extra labels of non-causal features) in order to divide the dataset (Lin et al. 2022b). Moreover, IRM-based methods are limited to addressing only a finite set of predetermined non-causal features, neglecting the potential existence of numerous unknown non-causal features. In fact, a recent study (Lin et al., 20226) in the field of IRM has theoretically demonstrated that is nearly impossible to partition dataset into different environments to eliminate all non-causal features using IRM. Other challenges, such as the tendency to overfit, difficulty in applying to larger models (Zhou et al. 2022; Lin et al., 2022a), and the marginal shift risk of the input (Rosenfeld et al., 2021). have also been identified within the realm of IRM. Inter RAT (Yue et al. 2023) attempts to eliminate feature correlation through backdoor adjustment, intervening directly with the confounders. However, it is extremely hard to measure the confounders since they are usually not observable in the dataset. As for degeneration, although not explicitly associated with spurious correlation until this study, some efforts have been tried to fix the problem. The common idea is to introduce auxiliary modules that have access the full texts to regularize the original explainer ( (Yu et al. (02T) or the predictor (Yu et al. 2021 Liu et al. 2022). Regularized by these auxiliary modules, the predictor can somewhat disregard the mask corre latrelation, and degeneration is partially alleviated.
Although these methods have tried to fix the problems resulted from spurious correlations, they are still MMI-based methods and how the problems come into being is not well explored. In this study, we first identify the two stages that the spurious correlations may come from, and link the important degeneration problem to more general mask correlation. Then, we identify that the target label Y and all the non-cate causca features (including the rationale masks) in the input X are d-separated by the
2

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Table 1: The examples of feature correlation and mask correlation. Human underlined. Rationales from RNP are highlighted in red.
annotated Human-annotated
rationales
RNP
Dataset: Beer-Aroma, Label: Positive. Predition: Positive. Problem: feature correlation
Text: the appearance was nice dark gold with not much of head but nice lacing when it started to dissipate the smell was ever so hoppy with a hint of the grapefruit flavor that 's contained within the taste was interesting. up front tart grapefruit not sweet in the least. more like grapefruit rind even ......
Dataset: Beer-Aroma. Label: Negative. Predition: Negative. Problem: mask correlation
Text: 12 oz bottle poured into a pint glass a pours transparent pale golden color. the head is pale white with no cream, one finger 's height, and abysmal retention looked away for a few seconds and the head was gone s stale cereal grains dominate. hardly any other notes to speak of very mild in strength -sharp corn/grainy notes throughout entirety
causal features, meaning that the non-causal features are independent of Y given the causal features. This leads to avenue for addressing feature correlation and mask correlation simultaneously: only need to penalize the dependence between the target label and the unselected features conditioned on the selected rationale candidate, such that all the direct causal features will be included in the selected rationale candidate. Based on this observation, we develop a new criterion for the causal rationale, namely minimum conditional dependence (MCD). Various methods can be adopted to measure the dependence, such as mutual information, the Hilbert-Schmidt Independence Criterion (HSIC) (Greetonn et al.] 2007), and on. In this paper, we adopt simple and practical measurement for independence, the KL-divergence, to verify the effectiveness of the proposed criterion. Then, we conduct experiments on two widely used benchmarks validate the effectiveness of MCD.
In summary, our contributions are:
To the best of our knowledge, we are the first to identify the degeneration problem as a form of spurious correlation. Leveraging probabilistic graphical models, we are the first to comprehensively elucidate feature correlation and degeneration under unified perspective.
We find that the target label and non-causal features are d-separated by the direct causal features. Based on this insight, we propose the MCD criterion, which opens new avenue for discovering causal rationales, marking the main contribution of this study. Unlike previous methods, MCD-based methods do not require prior expert knowledge about non-causal features, thus presenting potential for broader applicability.
We present simple and practical architecture to develop MCD-based method. Experi- ments across various datasets demonstrate that our approach achieves an improvement of up to 13.7% in F1 score compared to state-of of-the-art MMI-based rationalization methods.
2 Related work
The basic cooperative framework of rationalization called RNP Lei et al.) 2016 is flexible and offers unique advantage: certification of exclusion, which means any unselected input is guaranteed to have no contribution to the prediction (Yu et al.) [202T]. Based on this cooperative framework, many methods have been proposed improve RNP from various aspects. [Bao al. (0118) used Gumbel-softtax to do the reparameterizatioo for binarized selection. [Battins et al. (2019) replaced the Bernoulli sampling distributions with rectified Kumaraswamy distributions. Dain et al. (020)) disconnected the training regimes of the generator and predictor networks using a saliency threshold. Paranjape et al. (2020) imposed a discrete bottleneck objective balance the task performance and the rationale length. Chang et al. (2019) tried to select class-wise rationales. Antognini et al. (202T) Antognini and Faltings (2021) tried to select rationales belonging different aspects at once. Zheng et al. (2022) called for more rigorous evaluation of rationalization models.
Rationalization.
(2022) leveraged learning techniques to improve the quality of the explanations. Havrylov et al.-
Fernandes al.
(0199) cooperatively trained the models with standard continuous and discrete
optimization schemes. |Hase et al. (2020) explored better metries for the explanations. |Rajagopal et al. (202)) used phrase-based concepts to conduct a self-explaining model. Other methods like data augmentation with pretrained models (Plyler et al. (202T), training with human-annotated rationales (Chan et al. 2022), have also been tried. These methods orthogonal to research.
3

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Spurious correlations. Several methods have been proposed to address the issues arising from either feature correlation or mask correlation. The impact of feature correlation somewhat mitigated by techniques such as invariant risk minimization (Chang et al. 2020) or backdoor adjustment {Yue) et al. 2023). However, as indicated in the introduction, these methods have certain limitations. To combat mask correlation, the usual strategy involves introducing an auxiliary module, which has access to the full input, to regulate the original modules and prevent them from overfitting to trivial patterns introduced by the explainer (Yu et al. 2021 2019 multiple explainers to select diverse rationales (Liu et al. 2023a), assigning asymmetrie learning rates for the two players (Liu al. 2023b), have also been tried. Unfortunately, these methods have limited effectiveness against feature correlation in the input data. These aforementioned methods most relevant to our research, yet we are the first to consider both feature correlation and mask correlation from a unified perspective.
Liu et al. 2022). Other methods like using
Preliminaries
We consider the text classification task, where the input is text sequence ,2, 22, 21] being the i-th token and I being the number tokens. The label of X is a one-hot vector Y {0, 1); where is the number of categories. D represents the training set. Ante-hoc rationalization consists of an explainer fr)()) and a predictor fr()), with f. and Op representing the parameters of the explainer and predictor, respectively. The goal of an MMI-based explainer is to select the most indicative pieces from the input that are related to the label.
with x,
For (X, Y) ~ D, the explainer first outputs a sequence of binary mask M = (x) [m1,'' "M, my] e {o, 13 (in practice, the explainer first outputs a Bernoulli distribution for each token and the mask for each token is independently sampled using gumbel-softmax). Then, it forms the rationale candidate Xz by the element- product of X and M:
X2= = M X = [mi4i,"",,men
(1)
To simplify the notation, we denote S (X) as Xz in the following sections, i.e., JE(X)= Xz.
With the generator's 's selection, we get a set of (Z, Y) pairs, which are generally considered to be samples taken from the distribution P(Z, Y). Then, vanilla RNP attempts to identify the rationale by maximizing the mutual information I(Y; X2):
X2 arg argmax max T(Y; X2) arg max (H(Y)-H(Y|X))) arg min H(Y)Xz), 8.1. 8. SE(X). Xx
Xx
Xx
(2)
In practice, the entropy H())X2) is commonly approximated by the minimum cross-entropy mino, HY, Ylx2), with Y = fr(xx) representing the output of the predictor. It is essential to note that the minimum cross-entropy is equal to the entropy (please refer to Appendix B.3). Replacing Xz with (X), the explainer and the predictor are trained cooperatively:
min f( 0,
s.t., DD
To make the selected rationale human-intclligibbe, rationalization methods usually constrain the rationales by compact and coherent regularization terms. In this paper, use the same constraints used in INVRAT (Chang et al. 2020):
M|] 1
?(M) A
Ao
Σ
m:
m--1
(4)
The first term encourages that the percentage of the tokens being selected rationales is close to pre-defined level s. The second term encourages the rationales to be coherent.
Method
4.1 Motivation: how spurious correlations come into being.
In this section, we consider X as a set of variables (or a multi-dimennsonnl variables), and the selected rationale candidate Xz is a subset (some dimensions) of it.

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To begin with, in Figure a). we posit a probabilistic graphical model to illustrate the corresponding data-gendn geneerainn process for the BeerAdvocate dataset. The input X comprises comments on three aspects: Xs for Smell or Aroma, XT for Taste, and XA for Appearance, each of which can be considered as a subset variables of X. Additionally, H signifies something that does not discuss the sentiment tendency of X. For instance, H could include the color of a bottle. The annotators assign the smell label Ys by viewing the comments on aroma (Xs - Ys). Therefore, only Xs serves as the direct cause for Ys. However, Xs is correlated with Xr due set of unobserved variables U (called confounders). For example, may include a variable indicating whether the beer originates from reputable brand, and pleasant taste may imply that the beer comes from good brand (U Xy). Moreover, a beer from a reputable brand is likely to have a pleasing smell (U - Xg). Consequently, XT is associated with Ys via backdoor path, as depicted by the red dotted line in Figure 2 a). In this situation, Xy is somewhat indicative of Ys, but it signifies a statistical correlation rather than causality.
To have more intuitive understanding of this correlation, we assume toy example where U. Xs. Xr, and Ys are all Bernoulli variables, with their respective probability distributions as:
p(U= = = p(U 0) 0.5,
p(X, = 1|U -0)=pp(X;)) 0lU 0) 0.9,
tel festure comdlaion
(b) mank commlation
p(Xs = 1 1) p(xs 0JU = =0) = 0.9,
Figure 2: A probabilistic graph for (a) the data generating process of BeerAdvocate and (b) the (5) rationale (mask M) selection process.
p(Ys 1| = 0|Xs 0) 0.9,
With some simple derivations, we can easily obtain (detailed derivation is in Appendix B.1
p(Xs = 1) = p(X7 = 1) p(ys = 1) = 0.5.
(6)
Then, further get (see Appendix B.2 for the detailed derivation of Equation 8 and
p(U 1|XT = 1) (UU = 1, 11 p = = 1)p(U
0.9.
(7)
x(1x=1)
-
p(Xs
1|x+= =
Σ p(Xs =))=
=
0.9 0.9 0.9
0.1 0.1 0.82.
(8)
Ue(0.1)
P(Ys= = =
Σ p(Ys =
0.82 0.9 0.18 0.1 = 0.756.
(9)
(ge(0.1)
Equation 8 demonstrates that Xy (Taste) is highly correlated with Xs (Smell). and Equation
indicates that Xr (Taste) is also strongly indicative of Ys (Smell label). This situation can result in numerous local optima during the rationalization training process. Note that becoming trapped in a local optimum poses a significant challenge rationalization (Yu al. 2021 Liu al. 2022). It is worth noting that the correlation between taste and smell here is merely one of the examples, and sometimes can also correlate with Y in a similar fashion. For instance, in LIME (Ribeiro) al., 22166), predictor is trained to determine whether image contains wolf or not based on the presence of snow in the background.
Furthermore, the degeneration problem can also be interpreted with kind of spurious correlation (called mask correlation). as illustrated in Figure (b)) with an example. Returning to the second is selected as part of the rationale
example in Table [ the variable M, denoting whether candidate, caused by the input X (comprising subsets A. Xr, Xs and H) and the explainer E. M. is also correlated with Ys through a backdoor path, as indicated by the red line in Figure (b.
4.2 The conditional independent property
We first introduce an important concept in probabilistic graphical models, namely d-separation. Subsequently, we demonstrate how d-separation contributes to the identification of causal rationales.
D-Separation (Bishop) 2006): A, B, and C' denote arbitrary. non-intersecting sets of nodes (and their union might not cover all nodes of the graph) in a given probabilistic graph. Our objective is to determine whether a specific conditional independence statement A 1 B|C is implied by this graph. To do so, we examine all possible paths from any node in to any node in B. path is said to be blocked if it includes a node such that either (see Appendix B.4 for why such path is blocked)
5

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(a) The arrows on the path meet at node forming either a chain (i.e., - -) or fork (i.e., + -), with the node o being part of C, or
(b) The arrows the path meet node to form a collider (i.e., +), and neither the node o itself nor any its descendants are included in set C.
If all paths are blocked, then A is considered to be d-separated from B by C, meaning that A a B/C.
Returning to our rationalization problem, the backdoor path (dotted red line) in Figure comprises a fork (Xr - U - X)) and a chain (U Xs - Ys). If either Xs or U is included in the conditioning set, the path between Xs and Yss becomes blocked, leading to their conditional independence. and consequently, the eradication of corresponding feature correlation. Similarly, the backdoor path (dotted red line) in Figure (b) forms fork (M- Xs Ys). By including Xs in the conditioning set, the path between M_ and Ys is blocked, resulting in their conditional independence and consequently, the elimination of corresponding mask correlation.
We consider the general case, where the input X is a set of variables (or features). X is a subset of X that exclusively contains all the direct causes of the target label Y, i.e. the desiderata of the rationale. We select subset of X to serve as the rationale candidate (denoted as Xz), while the remaining unselected part is referred to as X-z. This leads to the following properties:
Lemma 1 X-z and Y are d-separated by Xz, we then have that all of the direct causal features in X must be included in Xz:
X-z and Y are d-separated by Xz = X Xz.
(10)
The proof in Appendix B.5 And it's very easy to intuitively understand it: direct cause has a one-hop path to the label. To block this path, this cause must be included in Xz.
Assumption The label Y has no causal effect on any variables in X.
Assumption is naturally valid in most real world applications due to the temporal sequence between X and Y. We also provide some failure cases of this assumption in Appendix B.6 Assumption ! specifies that there is arrow pointing from Y to any nodes in X.
Lemma If Assumption / holds, we have:
X-z and Y are d-separated by X2 = Xn c Xz.
(1I)
The proof is in Appendix B.7 Combining Lemma 1 and Lemma
2 we then have:
Theorem If Assumpttin holds, then all the direct causal features within X will be included in Xz if and only if X-2 and Y are d-separated by X2:
X-z z and Y are d-separated by -
( X
(12)
Remark. In light of Theorem 1 we understand that if aim achieve Y 1l X-xIXz, will consequently incorporate all direct causes of Y into Xz- It should be noted that the compactness of X2 is facilitated through the sparsity constraint expressed in Equation
4.3 The proposed method
Minimum conditional dependence criterion. Although previous research tried to design various auxiliary modules or regularizers to fix the problems of maximum mutual information criterion (Chang et al.) 2020;; Yue et al. [2023] Liu et al. 2022). we do not follow them to move on this line. propose distinct criterion for identifying the causal rationale, which involves minimizing the dependence between Y and the unselected input X z, conditioned on Xz:
Based on Theorem
X arg
(13)
Xz
where C is a criterion for dependence. For instance, C could take the form of partial correlation (applicable only to linear associations), mutual information, divergence, or the Hilbert-Schmidt Independence Criterion (HSIC) (Gretton al. 2007). among others.

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It then leads to the question of how we can apply this criterion practice. In this study, we only present a straightforward and practical method to validate our asser- tion with respect to Theorem [ leaving the exploration other measurements for fu- ture work. We first rewrite Y 1 X- Zz as
update the predictor to approximate the distribution
Predicioo
Pygix
mppUULL (HHLL, &2
[hardd
Explainer
Predicter
update the explainer for better Xx
Figure 3: The architecture of our proposed MCD. The approximators for the distributions shared re- (14) duce the model complexity (this trick is not necessary). Obviously, P(Y|X2) P(Y] if and only if the divergence between the two distributions is zero:
P(Y = P()
Y L X-2/22Xz = P(Y]X2)= = P(Y = (xI(P(XXXXIIIXXXXXXX) 0. (15)
Estimating divergence through approximation. The real distributions of P(Y and P(Y
are not directly accessible. So we need further efforts to approximate them. We try to approximate
them by making use of the predictor. We first approximate P(Y]Xz) with p( X2) by minimizing
the cross cross-entropy HY,Y, Y2(X2) and we also approximate P(Y[X) with PX) by minimizing
Ho(Y, Xx where Yz, Yx are the predictor's 's outputs with the inputs being 2 and X, respectively,
Thus, the training process for our MCD is depicted in Figure 3 the explainer first generates a rationale candidate X2 from the input X. Subsequently, X2 and are fed into the predictor to obtain two distributions, (())(X2) and PX) By replacing Xz with (x( and Y with fr()), the overall objective of our model becomes (The pytorch implementation is in Appendix A.2
min (ix, F2 HY,Y 8,
min (xw.yyy
[MM]]
(16)
8,
s.t., Xx = fx(X), P(Xx(X)= f(x
Notably, although the first term H. Y2 is similar to the used in Equation it is detached from the explainer's s parameters 0.. It is now only used to help the predictor approximate the real distribution P(Y) rather than to guide the explainer to find good rationale.
5
Experiments
5.1 Datasets and metrics
Datasets 1) BeerAdvocate (McAuley et al. 2012) is a multi-aspect sentiment prediction dataset widely adopted in rationalization studies. Given the high correlation among the rating scores of different aspects within the same review, rationale selection encounters severe feature correlation challenges. Following INVRAT (Chang et al., 2020) and Inter RAT (Yue al. 2023). we utilize the original dataset (which we refer to as correlated BeerAdvocate) to verify MCD ' elfectiveness in handling both feature correlation and mask correlation simultaneously. HotelReviews (Wang et al. 2010) is another multi-aspect sentiment classification dataset containing less feature correlation, which used by the latest SOTA method FR (Liu al. 2022) to evaluate the effectiveness of addressing degeneration. We utilize the Service aspect to further demonstrate the competitive edge of our MCD. Among these datasets, each aspect itself can seen as a dataset and trained independently.
Metrics. Considering that the annotators assign the label of the target aspect by observing the causal features, the overlap between the tokens selected by the model and those annotated by humans provides a robust metric for rationale causality. The terms P. R, F'l denote precision, recall, and Fl score respectively. These metrics are the most frequently used in rationalization. The term S represents the average sparsity of the selected rationales, that is, the percentage of selected tokens in relation to the full text. Ace stands for the predictive accuracy.
5.2 Baselines and implementation details
We compare with various recent MMI-based methods that are highly relevant to our study. These include methods like INVRAT (Chang et al. 2020) and Inter RAT (Yue et al., 2023). which are
7

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Table 2: Results correlated BeerAdvocate. Each aspect is trained independently. "* "*" results The second best Fl scores are underlined.
obtained from Inter RAT Yue et al., 2023).
Methods
Appeurance
Aroma
Pulate
Acc
R
F1
Acc
R
F1
Acc
F1
RNP
10.0
32.4
18.6
23.6
10.0
44.8
32.4
37.6
10.0
24.6
23.5
24.0
IN
10.0
42.6
31.5
36.2
10.0
41.2
39.1
40.1
10.0
34.9
45.6
39.5
Inter-RAT
11.7
66.0
46.5
54.6
11.7
55.4
47.5
51.1
12.6
34.6
48.2
40.2
FR
11.1
75.8
70.4
42.0
52.6
9.7
87.7
68.1
42.2
52.1
11.7
87.9
43.7
40.9
42.3
MCD(ours)
9.5
81.5
94.2
48.4
63.9
9.9
87.5
84.6
53.9
65.8
9.4
87.3
60.9
47.1
53.1
RNP
20.0
39.4
44.9
42.0
20.0
37.5
$1.9
43.5
20.0
21.6
38.9
27.8
INVRAT
20.0
58.9
67.2
62.8
20.0
29.3
52.1
37.5
20.0
24.0
55.2
33.5
Inter-RAT
21.7
62.0
76.7
68.6
20.4
44.2
65.4
52.8
20.8
26.3
59.1
36.4
FR
20.9
84.6
74.9
84,9
79.6
19.5
89.3
58.7
73.3
65.2
20.2
88.2
36.6
59.4
45.3
MCD(ours)
20.0
85.5
79.3
85.5
82.3
19.3
88.4
65.8
81.4
72.8
19.6
87.7
41.3
65.0
50.5
RNP
30.0
24.2
41.2
30.5
30.0
27.1
55.7
36.4
31.0
15.4
42.2
22.6
IN
30.0
41.5
74,8
53.4
30.0
22.8
65.1
33.8
30.0
20.9
71.6
32.3
Inter- RAT
30.5
48.1
82.7
60.8
29.4
37.9
72.0
49.6
30.4
21.8
66.1
32.8
29.6
86.4
81.4
62.3
30.8
88.1
37.4
75.0
49.9
87.0
24.5
58.8
34.6
MCD(ours)
29.7
86.7
59.6
95.6
73.4
29.6
90.2
46.1
87.5
60.4
29.4
87.0
30.5
72.4
42,9
focused on addressing feature correlation, as well as methods such as FR (Liu at. mitigate mask correlation (i.e., degeneration). Among these, FR represents the latest SOTA approach in addressing mask correlation, while Inter RAT stands as the SOTA in handling feature correlation.
2022) that aim to
Both the explainer and the predictor are composed of an encoder (which can be an RNN or Trans- former) and linear layer. Some of the baseline methods have not provided runnable source codes. To ensure a fair comparison, we keep the major settings consistent with those of the baselines, which
commonly utilized in the field of rationalization (Chang al. 2020: Yu et al.] |021] Liu et al.)
2022] Yue et al. 2023). Specifically, we employ the 100-dimensional Glo Ve (Pennington et al. 2014)
for word embedding and 200-dimensional GRUs (Cho et al., 2014) to obtain text representation. The re-parameterization trick for binarized selection is Guubeel-offnng
(Jang et al., 2017). The hyperparameters of the reimplemented baselines are initialized with the values reported in their source codes, and are then manually tuned multiple times to determine the optimal settings. We do not use BERT (Devlin et al. 2019) in the main experiments because some recent research (Chen et al. 2022
Liu et al., 2022 Zhang et al. 2022) has found it to be a challenging task to fine-tune large pretrained models within the rationalization framework (see Appendix [A.4 for more discussion). However, as a supplement, we also conduct experiments with two pretrained models, ELECTRA (Clark et al. 2020 and BERT. The optimizer is Adam (Kingma and Ba, 2015). All models are trained on a RT RTX3090 GPU. More details are in Appendix A.I
5.3 Results
Comparison with SOTA Methods. Table 2 shows
Table 3: Results on HotelReview. "**, results
the results on correlated BeerAdvocate with the ratio- nale sparsity being about 10%. 20%, and 30%. We Methods
obtained from FR (Liu et al. (022).
Ace
Fl
set the sparsity to be similar to previous methods by adjusting the sparsity regularization term (i.e., s) in Equation 4 Compared to MMI- -bsed methods, we
RNP
11.0
97.5
34.2
32.9
33.5
DMR*
11.6
43.0
43.6
43.3
A2R*
11.4
96.5
37.3
37.2
37.2
FR
II.5
94.5
44.8
44.7
44.8
gain significant improvements across all three aspects and three different sparsity. In particular, we improve
MCDiours)
11.8
97.0
47.0
48.6
47.8
the Fl score by more then 10% as compared to the previous SOTA in three settings: in the Aroma aspect with S x 10, the Palate aspect with S 10, and the Appeurance aspect with S x 30. We show an visualized example of the selected rationales in Figure 4 Since our MCD criterion (Equation [3) is not limited to specific mcasurement of dependence. KL-divergence with JS-divergence, and the results are in Appendix A.3 Table 3 shows the results on another dataset also used in FR, where DMR (Huang et al. 2021) and A2R (Yu et al. 202T) are two recent MMI-based methods. For this dataset, we follow FR to set the sparsity similar to that of the rationales. On this dataset, we still beat all the MMI-based methods. We also show the time efficiency in Appendix[A.og
also conduct experiments by replacing
Inducing mask correlation with skewed explainer. In order to evaluate scenarios where feature correlation not severe and our primary concern mask correlation, we follow FR's approach to conduct experiments in synthetic setting where the explainer is specifically initialized to induce
8

---

Table 4: Results of skewed explainer that induces degeneration (i.e., mask correlation) in the Palate aspect of BeerAdvocate. * results obtained from the paper of FR.
Seting
RNP
FR
MCDiours
Fre.
Aw
R
R
FT
Pie A6V
Adc
F
R
F1
Pre.
Ape
R
F1
0ew63.0
66.6
14.0
R59
20.3
45.4
42.7
66.3
14.2
B1.3
19.3
67.9
63.4
66.1
12.0
34.6
61.6
63.7
62.6
skew70.0
71.3
84.1
10.8
70.8
14.1
88.3
54.7
62.1
70.2
64.00
skew75.0
75.5
57.6
9.6
8.8
75.6
13.1
84.8
49.7
52.2
51.0
75.3
61.3
Table 5: Results of methods using pretrained ELECTRA as the encoder.
Methods
Appearance
Aroma
Plate
Avc
P
Acc
P
R
FT
FR-ELECTRA
16.3
86.5
19.1
17.0
18.0
14.8
85.9
58.6
54.8
56.7
11.2
78.0
12.0
10.7
11.3
MCD-ELECTRA
18.5
90.0
84.8
85.6
85.2
14.5
86.6
86.2
78.7
82.3
12.1
85.0
63.0
60.3
61.6
mask correlation, also referred to as degeneration. The details of the initialization can be found in Appendix A.3 Following FR, we utilize the Palate aspect of decorrelated BeerAdvocate dataset (a subset of the original BeerAdvocate that has been filtered by Lei et al. (2016)). This subset contains less feature correlation compared to the original dataset. The results are presented in Table where skewk and Pre_md acc indicates the degree of mask correlation. In this situation, the vanilla RNP fails to identify the causal rationales, and FR is also significantly impacted when the degree of mask correlation is high. Our MCD is much less affected, demonstrating its robustness in such scenarios.
Experiments with pretrained language models. In the field of rationalization, researchers generally focus on frameworks of the models and the methodology rather than engineering SOTA. The methods most related to our work do not use BERT or other pre-trained encoders (Chang et al. 2020; Yu et al., [2021] Liu et al. 20222 Yue et al.
Table 6: The F1 scores of models trained with different encoders. "*"; results ob- tained from (Chen et al. 2022). "ykn,
results obtained from FR. The dataset is decorrelated Beer- Shter-Apparrnce
Method
GRU
ELECTRA
BERT
2023
Experiments in some recent work (Chen et al.
VIB*
20.5
SPECTRA*
28.6
2022
Liu al. 2022) suggest that there are some unfore-
RNP
13.7
14.7
seen obstacles making it hard to finetune large pretrained models within the rationalization framework. For example, Toblo that two improved rationalization methods (VIB (Paranjape et al. 2020) and SPECTRA
FR
82.8
14.6
29.8
MCTXaurs)
80.1
85.2
87.1
(Guerreiro and Martins 202I)) and the latest published FR all fail to find the informative rationales when replacing GRUs with pretrained BERT. To climinate potential factors that could lead to an unfair comparison, we adopt the most widely used GRUs as the encoders in our main experiments, which can help us focus more on substantiating our claims themselves, rather than unknown tricks. But to show the competitiveness of our MCD, we also provide some experiments with pretrained language models as the supplement. Due to limited GPU resources, we adopt the relatively small ELECTRA-smal in all three aspects of BeerAdvocate and the relatively large BERT-base in the Appearance aspect. We compare our MCD with the latest SOTA FR (Liu et al. 2022). We follow FR to set the sparsity similar to human-annotated rationales. More details are in Appendix A.4
The results with BERT are shown in Table and results with ELECTRA are shown in Table 5 We see that our method can greatly benefit from pretrained models. In f'ct, recent research has found that finetuning large pretrained models can be easily affected by overfitting (Zhang et al. [22T and spurious correlations can exacerbate this overfitting, particularly in larger models (Zhou et al. 2022 Lin et al. 2022a), which somewhat explains the great progress achieved by our MCD.
6 Conclusion, future work, and limitations
In this study, we first illustrate the two primary issues of feature correlation and degeneration in MMI- based rationalization under a unified causal perspective. Subsequently, we uncover the conditional independence relationship between the target label and non-causal and causal features. Based this observation, we propose a criterion of minimizing conditional dependence to concurrently address the two aforementioned problems.
Given the versatility of the self-explaining rationalization framework, our proposed methods show significant potential for application across diverse fields such computer vision and graph learning. Additionally, with the recent remarkable success of large language models (L.LMss, exploring how our MCD can aid in training trustworthy LLMs is another avenue worth pursuing.

---

Label (aroma aspect]: Positive.
Label (aroma aspect); Positive.
Prediction Posilive.
Prediction: Posilive.
Input: got this one on tap at keily 's olympian in portland lighting made the colour difficult to ascertain, but would be if it were at a very dark brown came with good head which stack around, udding to the feel of the pant. i I'm these smoky lors, so all could distingui sb was malty cherry whiff felt very smooth and went down very easily was sweder and more syrupy than i would expect an english brown ale be. the smell did prove indicaltvv of the iniual laste, al thhough where i experted it give way to a somewhat hopgy finish, thankfullyy subde coffee flavour kicked in instead i detest coffee, but this was subtle enough to be tolerable.
Inputt: got this one on tap at kelly 's olympian in portland. lighting made the colour difficult to ascertain, but would be supotsed were ntt very dark brown, came with a good bead which stuck around adding to the feel of the piot. i'm unclesss on the suiff lest in these smoky lars, so could distinguish was a malty chery whitf jet very smooth and went down very easily. was sweeter and more syrupy than i would expect english bronn
usdess on
be. the smell did prove indicative of the ini tial taste, although where i expected it to give way somewhat hoppy finish, a thankfully subtle coffee flavour kicked instead. detest coffee, but subtle enough to be tolerable.
(a) RNP
(b) Inter RAT
Label (aroma aspect): Positive.
Label (aroma aspect): Positive.
Predliction Posilive.
Predictioone Positive,
top kelly olympian in portland lighting made the culour difficult to ascertain, bul i would be surpeised if it were a' a very dark brown. came with a good
Imput:
got
got this on tap kelly olymplan portland lighting made the colour difficull to ascetainn but i would be surprised if it were n't a very dark brown. came with good head which stuck around, adding the feel of the pint. i I'm weduss un the sniff lest in these smake loars so all could distinguish was malty cherry whiff felt very smooth and went down very easily. was sweeter and more syrupy than would expect an english brown ale to be. the smell did prove indicative of the inilial give way to somewhat hoppy finish, thankfully subtle coffee flavour kicked in instead. i detest coffee, but this was subtle enough be tolerable.
Impot:
beed which stuck around, adding the feel of the pant. 'n
usdesss on the sniff lest in thow smoky hars. so all could distioguish was malty cherry whiff very smooth and went down very easily,
sweder and more syrupy than
would expect an english hrown ale to he. the smell did prove
indicative of initial laste, though where expected it to way to a somewhat hoppy finish, thankfully subtle coffee flavour kicked in instead. i detest coffee, but this was subtle enough be tolerable.
, although w ex pectedd
(c) FR
(d) MCD
Figure 4: An example of selected rationales in the Aroma aspect of BeerAdvocate. The sparsity is set to be about 10%. The underlined texts are human-annotated rationales. (a): RNP selects palate only. (b): Inter_RAT selects aroma but also palate ("felt very smooth"). (c): FR is similar to Inter_Re IAT. (d): MCD selects aroma only.
A potential limitation is that, similar to IRM-based methods, our primary focus is on identifying rationales with causal effects, rather than quantitatively computing the precise values of these causal effects. Although quantifying causality often relies on strong assumptions, this quantification may be a desideratum for certain applications. We aim to explore this direction in future work to accommodate a wider range of applications. Another limitation is that we focus on the text classification task. Different tasks may have very different causal structures. Thus, how extend this method to other tasks is also a challenge that needs to be explored. The third limitation is that the obstacles in utilizing powerful pretrained language models under the rationalization framework remain mysterious. Although we have made some progress in this direction, we have to say that the empirical results with pretrained models are very sensitive hyperparameter tuning. A recent paper has also shown that very small changes in hyperparameters can lead to significant differences in results (see Remark 6.1 and Appendix G.2 in (Zhang al., 2023). To avoid being distracted by irrelevant factors, until this issue is resolved, we call for research papers to use small models to better verify their claims.
7 Acknowledgements
This work is supported by National Natural Science Foundation of China under grants 62376103, 62302184, 62206102, and Science and Technology Support Program of Hubei Province under grant 2022BAA046. We are also grateful for the valuable suggestions provided by the anonymous reviewers, which greatly helped to improve the quality of this paper.
10

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14


Reviews:
[{"review_id": "PObcNElzBa", "reviewer": "Reviewer_xX9m", "summary": "This work focuses on the problem of rationalization that aims to extract explanatory sentences that serve as rationales along with training a predictor for the downstream task. Prior work on rationalization has typically employed the maximum mutual information (MMI) criterion. However, this criterion does not uncover causal relationships and latches on to feature correlations and degenerations. The key insight in this work is that non-causal features are independent of the target labels given the causal features, based on which the authors propose a minimum conditional dependence criterion to discover causal rationales.", "questions": "- Why is Equation 16 a good approximation? Define \\Omega(M) in Equation 16.\n- In Table 5, why are the F1 scores of FR-ELECTRA so poor on the Appearance and Palate aspects?\n- Some anecdotal examples of causal rationales that were identified via MCD as opposed to MMI would be useful for the reader.", "limitations": "Some limitations have been listed.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Paper is written well.\n- Offer a unified perspective of feature correlations and degenerations (described in prior work on rationalization).\n- Propose a new MCD criterion to identify causal rationales.\n- Outperform existing MMI-based rationalization techniques on two multi-aspect sentiment classification tasks.", "weaknesses": "One of the main contributions of this work is that the proposed MCD criterion leads to the discovery of causal rationales. It would be useful for the reader to see some examples of generated rationales via MCD, and also highlight failure cases when MCD fails to retrieve the causal rationales.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you deeply for taking the time to thoroughly review our paper. We are truly grateful for the insights and recommendations you've provided.\n\n**Q1:** It would be useful for the reader to see some examples of generated rationales via MCD, and also highlight failure cases when MCD fails to retrieve the causal rationales.\n\n**A1:** We appreciate your suggestion a lot. We have now added some examples and failure cases in Figure 1 and 2 of the rebuttal.pdf. \n\nIn the example in Figure 1, the causal rationale is the text describing the good aroma of the beer. We see that RNP selects only the text describing the Palate aspect. Inter_RAT selects part of Aroma, but also Palate (e.g., \"felt very smooth\"). FR also selects both Aroma and Palate. Our MCD selects only Aroma.\n\nIn the failure case in Figure 2, we observe that MCD effectively identifies simpler sentiment-oriented descriptions related to aroma but struggles to recognize higher-level logical reasoning. For instance, expressions like \"the first one that has been\" often carry evident emotional inclinations, but such phrases are not selected by MCD due to their reliance on common sense and logical reasoning.\n\n**Q2:** Why is Equation 16 a good approximation? Define \\Omega(M) in Equation 16.\n\n**A2:** As you know, the cross-entropy can be expanded as follows:  \n$H_c(Y,\\hat{Y}|X)=H(Y|X)+D_{KL}(P(Y|X)||P(\\hat{Y}|X))$.  \nWhen we get the minimum cross-entropy $H_c(Y,\\hat{Y}|X)$, we have $D_{KL}(P(Y|X)||P(\\hat{Y}|X))=0$, which means $P(Y|X)=P(\\hat{Y}|X)$. In this case, $P(\\hat{Y}|X)$ is a good approximation for $P(Y|X)$. And it's the same for how $P(Y|X_Z)$ is approximated by $P(\\hat{Y}|X_Z)$.   \nAs for $\\Omega(M)$, it's defined in Equation 4 (Line 154). We appreciate your suggestion and agree that it will be good for the readers if we imply it again in Equation 16, and we will add it in the next version.\n\n**Q3:** In Table 5, why are the F1 scores of FR-ELECTRA so poor on the Appearance and Palate aspects?\n\n**A3:** Thank you for your question. In fact, we think we should approach this phenomenon from the opposite perspective, that is, why FR performs well on the Aroma aspect. As shown in Table 6, we see that most previous methods perform very poorly when conducted with over-parameterized BERT. On the Appearance aspect, our reimplementation of FR is better than the one reported by FR itself (the results in Table 6 are reported by FR itself).   \nSo why can FR perform well on the Aroma aspect? We are not sure, but we note that a recent paper called CR [A], publised on ICML 2023, shows similar results:\n|      F1     | Appearance | Aroma | Palate |\n|:-----------:|:----------:|:-----:|:------:|\n| CR-BERT     | 28.0       | 39.0  | 26.5   |\n| FR-ELECTRA  |    18.0    |  56.7 |  11.3  |\n\nWe see that CR also performs well on Aroma, but poorly on Appearance and Palate. The reason may be that Aroma is a relatively easy aspect and the other two aspects are relatively harder. However, a more precise understanding of the underlying mechanism may be highly complex, which to some extent goes beyond the scope of this paper.\n\n[A] Towards Trustworthy Explanation: On Causal Rationalization. ICML 2023.\n\n\n\n\n\n\n"}, {"review_id": "276OxzgPyv", "reviewer": "Reviewer_Rbzy", "summary": "\nThe maximum mutual information criterion is commonly used for rationalization, but it uncovers associations rather than causal relationships. The authors propose to identify non-causal features that are independent of the labels given the causal features and the ‘minimum conditional dependence’ criterion, which does not require prior expert knowledge. Experiments are focused on the popular BeerAdvocate and HotelReviews datasets, though mostly on the former, and obtain very competitive results against the other, well-chosen baselines.", "questions": "- This seems like a companion piece to “MGR: Multi-generator Based Rationalization” by Liu et al (2023), as much of the text is identical and they also deal with spurious correlation and degeneration, but the focus is on the MCD criterion rather than using multiple generators. What other differences can easily be described in your paper, if this paper were cited?\n\n- Is your ‘derivation’ in appendix B1 really an example of Bayes’ theorem, or just an application of marginalization and the chain rule?\n\n- Assumption 1 (L223) should be better explained in Appendix B, as the ‘temporal sequence’ explanation provided in the paper is insufficient, especially if it refers to some aspect of dataset acquisition or prediction rather than the actual state of the world. Can you expand on this?\n\n- the FR method (L274) is claimed to be the SotA, but even the paper by Liu et al (2022) to which it refers does not make a compelling case for it being the true SotA — how is this claim proven conclusively?\n\n- Given the new methodological approach, it would be interesting to include some study of the required computational resources in the main body of the text, beyond the brief mention of RTX3090 and appendix A.5’s epochs, including possible ablations. Would that be possible?\n\n- Can you add an explanation (or rationale) for why fine-tuning BERT would be ‘challenging’ (L299) beyond just providing citations and some indication of potential overfitting (section beginning L331)? ", "limitations": "- The last paragraph of Sec 6 touches briefly on the limitations. It is suggested that not computing the ‘precise values’ of causal effects would be a limitation, although it is not clear why that would be. Various other forms of limitation, including the relatively restricted datasets (in terms of task, scope, and number) used in the experiments or the focus on text, could also be addressed, for example.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The treatment of this problem using causal terminology is appropriate and interesting, and attempts were made to formalize some of these concepts (in Sec 4.2), with only some challenges, listed below.\n\n- Appropriate baselines and datasets are provided in the experiments, as per other related (uncited) work. Moreover, attempting to overcome limitations of ‘some of the baseline methods’ (L290) by establishing consistent ‘settings’ is good, although what those settings are (beyond using GloVE) should be included.\n\n- The rates of improvement in Table 2 are impressive. In some cases, tests of statistical significance would be useful to include, at least against FR, though apparently not necessary.", "weaknesses": "- The treatment of related works is extremely superficial in Section 2. Approximately half of the section on rationalization is merely a list of papers barely described by their topics and summarized in whole as being ‘orthogonal’. To some extent, a literature survey was better covered in Section 1, \n\n- There are various minor issues including:\n\t- atypical English (L2 “pieces of their inputting texts”; L48 “is easily to be affected”; L56 “[lowercase] comments regarding”; L188 “a image”)\n\t- very small text (Fig 1)\n\t- incomplete citations (e.g., Yue et al (2023), L514.)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for dedicating your time and expertise to review our paper. Your insightful comments and suggestions are highly valued and appreciated.\n\n**A1 (related work):** Thank you for pointing out this issue. Due to page limitations, we foucsed mainly on papers closely tied to our work, especially those that investigate spurious correlations. However, we agree with you that the first paragraph of Section 2 was a bit too brief. We will expand this content in the next version to provide a more complete understanding for new readers outside this field.\n\n**A2 (minor issues):** Thank you for your observation. We'll ensure to thoroughly revise and proofread the document in our next iteration to correct any minor issues. And the settings (Strengths 2, L290) include word embedding (GloVE), networks (GRUs), and similar sparsities (10%, 20%, 30%).\n\n**A3 (regarding MGR):** \n- First, the problem identification. Although MGR can address spurious correlation and degeneration simultaneously with only one model, in the context of MGR, spurious correlation and degeneration are considered as two separate problems and treated with distinct theoretical analyses. However, our MCD is the first to unify these two problems into one: they both arise from the non-causality of MMI. As a result, we can better understand these two problems with a unified theoretical insight.\n\n- Second, the potential implications. The core idea behind MGR to mitigate the influence of spurious correlations is the central limit theorem, which involves using more generators to reduce the probability of missing causal rationales. However, the model's lack of flexibility makes it less compatible with other rationalization variants. Our MCD, an optimization criterion, is model-agnostic and more versatile. Besides, we hope that the motivational process of our MCD can inspire research in other causal discovery fields, such as causal representation learning.\n\n- Third, the model complexity. MGR relies on the central limit theorem and involves multiple generators, which requires more computational resources than other methods.\n\n**A4 (derivation in App.B1):** It's just an application of marginalization and the chain rule. Thank you for your careful observation, and we will change the statement accordingly.\n\n**A5 (regarding Assumption 1):** As far as we know, most of the real-world datasets are built in a collecting-annotating form. In such a form, $Y$ is given according to $X$, and the annotators won't edit $X$ after giving $Y$. So, Assumption 1 holds.  \nWe really appreciate your insight very much, which inspires us to think of some cases that might break Assumption 1. One is the synthetic data like ColorMinist. In ColorMinist, a human first annotates an image, and then edits the image again according to the assigned label. Another scenario is the collection of time series data, where annotators label the data based on existing information and then adjust the data collection method according to the previous labels. This creates a cyclic causal graph. However, in the literature of causal inference, most researchers only consider acyclic graphs. Nevertheless, we greatly appreciate your insight a lot and will add the discussion to the Limitations section in the next version.  \nWe note that in cases where Assumption 1 doesn't hold, we still have Lemma 1, i.e., D-separation severs as a sufficient condition for selecting causal rationales. When assumption 1 holds, it becomes a necessary and sufficient condition.\n\n**A6 (FR SOTA):** FR was introduced in late 2022 and showed superior performance over recent baselines DMR (AAAI 2021) and A2R (NeurIPS 2021) on both the Hotel and Beer datasets. Our own tests also showed that FR outperformed INVRAT and Inter_RAT in most scenarios on the correlated Beer dataset (Table 2). Therefore, we suggested it as a current SOTA. We acknowledge this might not be entirely accurate, and we'll modify this statement in our revised version.\n\n**A7 (computational resources):** Thank you for your valuable suggestion. Taking the appearance aspect of BeerAdvocate as an example, the computational resources costed by different methods are:\n|  |batchsize |  lr | epochs | memory(MB) | RTX3090 hours |\n:-:|:-:|:-:|:-:|:-:|:-:\nFR|256|0.0001|300|3504|0.21\nInter_RAT|256|0.001|20|3660|2.34\nMCD|128|0.0001|150|2630|0.28\n\nSince the memory usage is affected by the batchsize, we further assign our MCD with a batchsize of 256 (the same as FR and Inter_RAT), and the memory usage then becomes 3806 MB.   \nSince our approach is straightforward and does not introduce additional regularizers, we have not identified specific modules that require dedicated ablation. However, Table 4 is a synthetic experiment crafted to demonstrate MCD's efficacy against degeneration, it can be considered somewhat of an ablation study (where feature correlations are ablated). In the future, we will consider integrating our MCD criterion with other sophisticated methods and conducting appropriate ablations.\n\n**A8 (why bert doesn't work well):** This could be a combination of several complex reasons. Here are some possible factors: First, fine-tuning large models is challenging in itself, especially when there is not much data available. Second, most of previous methods are  very sensitive to hyperparameter tuning (implied in the A2R paper), which could lead to training instability and result in a higher likelihood of getting stuck in local optima. Third, the more powerful the explainer is, the more easier degeneration will be (implied in a recent paper [A]).\n\nWe agree that it would be valuable to explore specifically what happens with BERT It's somewhat beyond the scope of this paper, and we leave it as future work.\n\n**A9 (Limitations):** We appreciate your suggestions, and we will certainly expand on these points and discuss these limitations in more detail in the next version.\n\n[A] Unsupervised Selective Rationalization with Noise Injection. ACL 2023."}, {"review_id": "3SU8xvfDcX", "reviewer": "Reviewer_kLrd", "summary": "This paper studies selective rationalization. Many methods use the maximum mutual information (MMI) criterion to find the most indicative rationale to explain a target label. As it has been shown in the past, this criterion is, by design, sensitive to spurious correlation. This paper proposes a novel criterion instead of \"fixing\" MMI, called Minimum Conditional Dependence (MCD). The authors identify two stages from which spurious correlations may come from and propose a causal framework to circumvent them.\n\nMethod\nThe paper assumes that the beer dataset has been generated on 3 aspects: Appearance, Taste, and Smell. This is wrong since the dataset has 5 aspects in total (palate and overall). Nevertheless, the motivating example is convincing. Leveraging the concept of d-separation is novel. However, it is unclear how dissimilar is the proposed approach w.r.t. [1,2] for example (L240 and L130 don't discuss really the differences) A downside of the approach is the need to train one model per aspect. It would interesting to discuss about extending the proposed model.\n\nIn the experiment section, I would appreciate having the results for the other aspects, and potentially, the original beer dataset to understand the effect of MCD (while not many papers utilize the original beer dataset, I do think it's important). The number of baselines is not consistent through the experiments: T3-T4 would require more models to assess the superiority of MCD. \nFinally, I am surprised that MCD performs on average better rationalization performance but worst predictive performance, which defeats the purpose of \"rationalization\": explaining the output of a model. Why is there such a gap?\n\nThe related work is quite complete. Nevertheless, I have identified few missing citations [3-5].\n\n1 Chang et al. 2021, Invariant Rationalization (ICML)\n2 Yue et al. 2022, Interventional Rationalization\n2 Chang et al. 2019, A Game Theoretic Approach to Class-wise Selective Rationalization (NeurIPS) \n3 Antognini et al. 2021, Multi-Dimensional Explanation of Target Variables from Documents (AAAI) \n4 Antognini and Faltings 2021, Rationalization through Concepts (ACL)", "questions": "- Why MCD underperforms in terms of predictive performance?\n- How could we extend MCD to multi-dimensional rationales?\n- Could you report results of MCD with BERT for the other experiments since it provides better performance?", "limitations": "The authors do not discuss about the limitations and potential negative societal impact of their work.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The causal framework is novel, and using d-separation is a nice replacement to circumvent MMI's flaws\n- MCD obtains better rationalization performance\n- The paper is well written and motivated.", "weaknesses": "- It is unclear how dissimilar is the proposed approach w.r.t. [1,2] for example (L240 and L130 don't discuss really the differences)\n- The proposed model only works for 1 aspect.\n- MCD obtains worst predictive performance, which defeats the purpose of \"rationalization\": explaining the output of a model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your detailed review and constructive feedback on our paper!\n\n**A1 (regarding [1,2]):** Our key differences with [1,2] are discussed in L66-85 of Sec.1 and App.C, where we delve into their limitations and theoretical flaws. [1] employs IRM and [2] uses backdoor adjustment to add regularizers to the MMI loss. Our approach is not inspired by either IRM or backdoor adjustment. We bring new theoretical insights linking causality to our model-agnostic MCD criterion, potentially benefiting other methods. Moreover, our results surpass those of [1] and [2] by substantial margins, with improvements of 19.1 and 13.1, respectively, averaging over nine settings in Table 2's F1 scores.\n\n**A2 (only works for 1 aspect):** Could you please provide a clearer delineation for the term \"1 aspect\"? We see that MCD outperforms all baselines in terms of rationale quality across all aspects on the standard BeerAdvocate and HotelReview benchmarks in Table 2-3. \n\n**A3 (worst predictive performance):** Our analysis suggests otherwise, could you elaborate on your concerns? In the nine settings of BeerAdvocate, our MCD secured the highest accuracy in four cases, was only marginally behind the best FR in another four, and matched FR in the remaining one. Moreover, our results consistently outperformed INVRAT. On the standard Beer and Hotel datasets, our accuracy drop, compared to the best method, was at most 0.9%. Overall, when averaged across aspects or sparsities, our performance matches or exceeds the best baseline FR.\n\nThe accuracy of FR and MCD on BeerAdvocate (Table 2): \n||S|Apearance|Aroma|Palate|Average\n:-:|:-:|:-:|:-:|:-:|:-:\nFR|10|75.8|87.7|87.9|83.8\nMCD|10|81.5|87.5|87.3|**85.4**\n:-:|:-:|:-:|:-:|:-:|:-:\nFR|20|84.6|89.3|88.2|**87.4**\nMCD|20|85.5|88.4|87.7|87.2\n:-:|:-:|:-:|:-:|:-:|:-:\nFR|30|86.4|88.1|87.0|87.2\nMCD|30|86.7|90.2|87.0|**87.9**\n:-:|:-:|:-:|:-:|:-:|:-:\nFR|average|82.3|88.4|**87.7**|86.1\nMCD|average|**84.6**|**88.7**|87.3|**86.9**\n\n**A4 (Why lower predictive performance):** I guess you are referring Table 4 to say that MCD gets the worst predictive performance. The synthetic experiments of Table 4 are crafted to demonstrate MCD's efficacy against degeneration. Note that degeneration might not adversely affect predictive accuracy; sometimes it can even improve it. In Table 4, as the rationale quality decreases with increasing skew, the accuracy of RNP conversely rises (in the settings of Table 4, the explainer is specially initialized to select trivial patterns to indicate the label. If the predictor fits such patterns, the eplainer and predictor can collude to get high accuracy. But if the predictor fits the true semantics, the accuracy will not be as high). However, such elevated accuracy might be unstable and drop with shifts in trivial patterns. The accuracy of our MCD, though lower than that of RNP and FR, is more aligned with the rationale quality, indicating less degeneration.\n\nNotably, a recent study claims that relying on causal features doesn't always guarantee optimal accuracy [A].\n\n**A5 (extend MCD to multi-dimensional rationales):** While it's typical in current methods to train a model for each aspect, we agree that a consolidated approach is desirable. Research in this direction, such as the valuable references you provided, offers promising techniques. We're contemplating leveraging their insights to refine MCD for multi-dimensional rationale extraction. However, this endeavor remains a future aspiration, and is somewhat beyond the scope of this paper.\n\n**A6 (more results with BERT):** We are sorry, but due to limited GPU resources, only six of the nine experiments from Table 2 have been completed in time, and the results are shown in Table 3 of the rebuttal.pdf.    \nMost prior methods underperform when using BERT. The fine-tuning intricacies of BERT make it difficult to verify specific reasons for performance improvements. Thus, we primarily employed GRUs to validate MCD's efficacy. Also, due to resource limitations, we weren't able to conduct all experiments with BERT. We plan to address this in future work and will note this limitation in our paper. Thank you for your suggestion.\n\n**A7 (original beer):** The experiments in Table 2 are run on the non-decorrelated dataset. We now report the results for the taste aspect (since all other aspects can serve as a causal part of the overall label, we don't include the overall aspect) in Table 2 of the **rebuttal.pdf**. (We do not reimplement INVRAT on this aspect, and the reasons are in **A9**).\n\n**A8 (references):** Thank you for providing us such valuable references. We find they cover very interesting topics that we hadn't considered.  We will discuss them in Section 2 and consider drawing inspiration from them to expand our approach to multi-dimensional rationales in the future.\n\n**A9 (baselines in T3-4):** Thank for your suggestion. We have now reimplemented Inter_RAT and added it to Table 3. The results are in Table 1 of the **rebuttal.pdf**. Since INVRAT doesn't provide runable code and the details of how to create different environments are not very clear, we fail to reimplement INVRAT. Another reason we didn't include INVRAT and Inter_RAT in Table 3 is that FR has been shown to outperform INVRAT and Inter_RAT a lot on BeerAdvocate (which is the main dataset used in INVRAT and Inter_RAT), so we thought comparing MCD to FR was somewhat enough.\n\nTable 4 works more as an ablation study (where feature correlations are ablated) to verify the effectiveness in addressing degeneration. FR is specifically designed to address degeneration and has achieved SOTA results in that direction. We compare our MCD with vanilla RNP to validate its effectiveness and with FR to assess its competitiveness. The competitiveness of our MCD in real-world scenarios is primarily validated by Table 2-3, rather than Table 4.\n\n[A] Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. arXiv:2307.  "}, {"review_id": "icHyeYc2aJ", "reviewer": "Reviewer_bHMi", "summary": "In this paper, the authors focus on self-explaining rationalization and they propose a Minimum Conditional Dependence (MCD) criterion to select causal rationales from a causal perspective.", "questions": "1.Why do the authors use different baselines in BeerAdvocate and HotelReview?\n\n2.The authors' discussion of rationalization+LLMs is insufficient, I would like to see more of the authors' views on rationalization+LLMs.\n\n3. The authors' experiments focus on the text classification task, would MCD be equally effective on other nlp tasks, such as text generation?\n\n", "limitations": "See Weaknesses for details.", "rating": 3, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Rationalization is a worthwhile direction to explore in interpretable research.\n2. The paper is well written.", "weaknesses": "1.This paper lacks novelty. First of all, the causalities in rationalization has been widely discussed[1][2][3]. Besides, the method proposed by the authors is very similar to the previously proposed method[4][5]. Authors do not compare them as baselines and not cite these research paper.\n\n2. The datasets in this paper are limited and I would expect to see more datasets such as ERASER[6] that have been widely used for rationalization[7][8][9].\nFurthermore, both BeerAdvocate and HotelReview seem to focus on the binary classification, have the authors studied the effectiveness of MCD on multi-classification tasks, such as DARE[10] and InfoCAL[5] do?\n\n3. Can the authors provide an example to show that the rationales extracted by MCD extraction is not affected by spurious correlations in the data.\n\n4. The recent work is insufficient where much of the recent work on the rationalization is not discussed, such as [3][4][5][6][8][10].\n\nReferences\n[1] Invariant rationalization.\n[2] Interventional rationalization.\n[3] Discovering Invariant Rationales for Graph Neural Networks.\n[4] How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking\n[5] Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration\n[6] ERASER: A benchmark to evaluate rationalized NLP models.\n[7] An information bottleneck approach for controlling conciseness in rationale extraction.\n[8] Unifying model explainability and robustness for joint text classiﬁcation and rationale extraction.\n[9] UNIREX: A Unified Learning Framework for Language Model Rationale Extraction\n[10] DARE: Disentanglement-augmented rationale extraction\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your valuable comments and suggestions.\n\n**A1 (Novelty):**  \nRegarding the comparison with [1,2,3], we agree that the causality in rationalization is not a new topic. However, our research differs significantly from [1,2,3]. The limitaions of [1,2] ([3] is similar to [1]) have been discussed in details in Sec.1 and App.C. For the methodology, our approach is not inspired by either IRM or backdoor adjustment. The analyses on rationale causality are also very different from [1,2]. We also  provide a very different theoretical perspective linking causality to our MCD criterion, which is model-agnostic and could enhance other methods. Furthermore, our performance significantly outperforms [1] and [2] in terms of F1 score.\n\nCompared to [4], our research targets a different problem, guided by unique motivations and novel theoretical insights. While [4] focuses on post-hoc explanation, our work is centered on self-explanation literature, leading to different challenges (faithfulness for post-hoc explanation (page 4 in [4]) and plausibility for self-explanation (L27-65)) and targets. Besides, [4] doesn't touch any analysis about the causality. The focus of [4] is developing differentiable masking. Our main contributions, the analyses on rationale causality, diverge from [4].\n\n[5] may appears similar as it uses a discriminator to make the guider predictor and the original predictor encode indistinguishable information; however, our goals and theoretical insights differ significantly from [5]. For example, [5] cannot guarantee that causal rationales will get lower $l_d$ (the discrimination loss in [5]) than spurious correlations. Also, [5] doesn't align closely with our research problem (spurious correlations and degeneration), and its code isn't accessible.\n\nCould you please provide more details about the similarity so that we can better address your concerns? We appreciate your suggestion and will discuss [3,4,5] in Sec.2.\n\nWe respectfully highlight that our work is not only about proposing a new method, but also about linking two key problems to the flexible MCD criterion. As far as we know, the degeneration has never been considered as a non-causality problem, even in [1,2].\n\n**A2 (datasets):** Our experiments focus on testing the efficacy of our MCD in selecting causal rationales, which calls for specific dataset properties. First, the main challenge should be to distinguish between association and causation rather than other problems, which requires there to be some spurious correlations in the dataset. Multi-aspect classification datasets satisfy this property well, since there are usually correlations between different aspects. Other datasets in ERASER don't satisfy this property. Second, there should be human-annotated causal rationales in the test set to measure whether the selected rationales are real causal rationales or just associated with the label. The LJP dataset used in [5,10] doesn't satisfy this property. [5,10] only evaluate the prediction performance on this dataset, which is only association, not causation.\n\n**A3 (examples):** Thank you for your suggestion. It was an oversight on our part to leave that out. We have added some examples to Fig.1 and 2 of the **rebuttal.pdf**.\n\n**A4 (recent references):** We appreciate your suggestion and will include them in Sec.2. Given the vastness of the rationalization field, it's hard to cover all new papers in a non-survey paper. However, we've exhaustively discussed all important papers that pertain to our key research problems, i.e., feature correlation and degeneration.\n\n**A5 (different baselines for Beer and Hotel):** Thank you for your question. We have now reimplemented Inter_RAT on Hotel. The results are in Table 1 of the **rebuttal.pdf**. Since INVRAT doesn't provide runable code and the details of how to create different environments is not very clear, we fail to reimplement INVRAT on other datasets. Another reason we didn't include INVRAT and Inter_RAT in Table 3 is that FR (published in late 2022) has been shown to outperform INVRAT and Inter_RAT a lot on BeerAdvocate (which is the main dataset used in INVRAT and Inter_RAT), so we thought comparing MCD to FR was somewhat enough.\n\n\n**A6 (LLMs):** Here is a brief discussion of LLMs+XAI. With the great success of LLMs, a new research line for XAI is chain-of-thought. By generating (as opposed to selecting) intermediate reasoning steps before inferring the answer, the reasoning steps can be seen as a kind of explanation. However, LLMs sometimes exhibit unpredictable failure modes [B] or hallucinatory reasoning [C], making this kind of generative explanation not trustworthy enough in some high-stakes scenarios. Also, some recent research finds that LLMs are not good at extractive tasks [D-F].  \nWe appreciate your suggestion, but delving further into the challenges of applying LLMs is somewhat beyond the scope of this paper. We will add it to the limitations and leave it as future work.\n\n**A7 (would MCD be equally effective on other nlp tasks?):**  It depends on the problem definition. As you know, different tasks can have different causal structures. We hope that the motivational process of MCD can inspire others to propose methods that work in other fields. However, we don't claim that our method is directly applicable to any tasks without modification.\n\n[A] ZIN: When and How to Learn Invariance Without Environment Partition? NeurIPS 2022.  \n[B] Causal reasoning and large language models: Opening a new frontier for causality. arXiv:2305.  \n[C] Survey of hallucination in natural language generation. ACM Computing Surveys, 2023.  \n[D] Is chatgpt a general-purpose natural language processing task solver? arXiv:2302.  \n[E] Evaluating chatgpt’s information extraction capabilities: An assessment of performance, explainability, calibration, and faithfulness. arXiv:2304.  \n[F] A comprehensive capability analysis of gpt-3 and gpt-3.5 series models. arXiv:2303."}]

Author rebuttal:
We are deeply grateful to every reviewer for their in-depth analysis and constructive feedback on our manuscript.

Here are the figures and tables of some of the new experimental results, attached to rebuttal.pdf.

Meta-review: The method proposed to discover causal rationales is interesting as acknowledge by two reviewers. However, one reviewer with a high confidence remained unconvinced on couple of points. Considering the paper's merits, I am in favor of accepting the paper.
Decision: Accept (poster)