Neural Causal Graph for Interpretable and Intervenable Classification
Authors: Jiawei Wang
Shaofei Lu
Da Cao
Dongyu Wang
Yuquan Le
Zhe Quan
Tat-Seng Chua
Venue: ICLR 2025 Poster (2025)
Keywords: Causal Inference
Graph Neural Network
Classification
Primary area: interpretability and explainable AI
TL;DR: NCG combines causal inference with neural networks to enhance interpretability and human-AI interaction. It outperforms traditional baselines with a unique intervention training method, achieving up to 95% top-1 accuracy on the ImageNet dataset.

Abstract: Advancements in neural networks have significantly enhanced the performance of classification models, achieving remarkable accuracy across diverse datasets. However, these models often lack transparency and do not support interactive reasoning with human users, which are essential attributes for applications that require trust and user engagement. To overcome these limitations, we introduce an innovative framework, Neural Causal Graph (NCG), that integrates causal inference with neural networks to enable interpretable and intervenable reasoning. We then propose an intervention training method to model the intervention probability of the prediction, serving as a contextual prompt to facilitate the fine-grained reasoning and human-AI interaction abilities of NCG. Our experiments show that the proposed framework significantly enhances the performance of traditional classification baselines. Furthermore, NCG achieves nearly 95\% top-1 accuracy on the ImageNet dataset by employing a test-time intervention method. This framework not only supports sophisticated post-hoc interpretation but also enables dynamic human-AI interactions, significantly improving the model's transparency and applicability in real-world scenarios.

Published as a conference paper at ICLR 2025
NEURAL CAUSAL GRAPH FOR INTERPRETABLE AND INTERVENABLE CLASSIFICATION
Jiawei Wang', Shaofei Lu'; Da Cao', Dongyu Wang", Yuquan Le', Zhe Quan', Tat-Seng Chua²
'Hunan University, Changsha, China
?University of Science and Technology of China, Hefei, China
National University of Singapore, Singapore
(wangjiawe awe 0531, caoda0721, dywang3254 (e @@gma11 com
s fflu,, Leyuquan, quanzhe Paathheeeenuuuu @hnu. edu. en, descts@nus edu, sg
ABSTRACT
Advancements in neural networks have significantly enhanced the performance of classification models, achieving remarkable accuracy across diverse datasets. However, these models often lack transparency and do not support interactive reasoning with human users, which are essential attributes for applications that require trust and user engagement. To overcome these limitations, we introduce an innovative framework, Neural Causal Graph (NCG), that integrates causal inference with neural networks to enable interpretable and intervenable reasoning. We then propose an intervention training method to model the intervention probability the prediction, serving as a contextual prompt to facilitate the fine-grained reasoning and human-AI interaction abilities of NCG. Our experiments show that the proposed framework significantly enhances the performance of traditional classification baselines. Furthermore, NCG achieves nearly 95% top-1 accuracy on the ImageNet dataset by employing a test-time intervention method. This framework not only supports sophisticated post-hoc interpretation but also enables dynamic human-Al interactions, significantly improving the model's 's transparency and applicability in real-world scenarios.
INTRODUCTION
In the realm of deep neural networks, classification tasks frequently serve as benchmarks for eval- uating the performance and robustness of emerging models (Krizhevsky et al. [2012] Wang et al. 2022 Liu et al. 2024 Lv al. 2025b). These models, particularly those based on foundation models (Vaswani et al., 2017: Bommasani et al. 2021
Lv al. 2025c). are powerful in handling
2009; Wang et al. 2019 Schuhmann et al. 2022) Wang et al. 2023 Shen et al. 2024a b Wang et al. 20244a Lv et al.] (025a) but often lack transparency and interac- tivity, which are key attributes for applications requiring high levels of trust and user engagement.
complex datasets Deng et al.
Traditional approaches have focused on post-hoc interpretability techniques, such diseriminative
feature visualization (Zhou et al. 2016; Selvaraju et al.) 2017 Zhu et al., 2021). to elucidate the eeczisionmmaking processes of models. However, these models primarily function as black boxes, lack- ing the ability for interactive interventions. This limitation hinders alignment with human reasoning and prevents real-time adaptation of the model behavior. Furthermore, causality- networks (Jiang et al., [2023] Job et al. (023) focus on interpreting data by quantifying cause-effect relationships among variables or recovering the causal graph from observed datasets. However, these models lack the capability for interactive intervention and do not support interactive classification involving human users.
graph neural
To address these challenges, we propose the Neural Causal Graph (NCG), novel framework that enable intervenable classification by integrating causal inference within neural networks architectures. Unlike conventional methods that only provide post-hoe interpretability. NCG constructs causal graph modeling the data's 's underlying mechanisms, enabling the model to reason through causal
"Corresoonding Author

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Y
Y
21
Z
1/2
X
X
Z2
X
11
X2
Xn
X1
xtratn
Xtruin
X
(a) Classic
(b) Sample Relation Graph
(c) NCG
Figure 1: The graphical overview of three distinct classification paradigms. Figure (a) illustrates traditional classification approach that treats the label prediction process of cach sample as inde- pendent. Figure (b) depicts a transductive learning method based on the relation (undirected) graph of different samples. In contrast, Figure (c) presents the neural causal graph approach, where the model constructs a graph with prior concept nodes Z; and posterior concept nodes Y,, with edges representing causal relationships among various concepts.
relationships and support intervenable classification. This NCG paradigm differs from both the
traditional classification approaches (Simonyan & Zisserman 2014 He et al., 2016 Zhou et al.
2023) and methods relying sample relation graphs (Liu al. 2018 Ciano al. 2021 Wang et al. 2024b). as illustrated Figure [ Central to our approach is the utilization of a structural causal model (SCM) (Pearl 2000 Peters al. 2017) that serves as the backbone of the NCG, This causal framework allows for the explicit modeling and manipulation of causal relationships among a set of concept nodes, which represent abstracted concepts or classes in the dataset. By integrating SCM with deep neural networks, NCG is capable of performing intervenable reasoning, where users can actively manipulate certain concepts (do-interventions) to observe how these changes influence the outcomes, providing powerful tool for "what-if" analysis (Pearl 2000).
Specifically, our method employs an efficient algorithm for estimating the causal effects within the NCG by leveraging do-calculus (Pearl (012)) and the potential outcome model (POM) (Rosenbaum Rubin 1983; Pearl (0122). This allows NCG to infer causal effects accurately without the biases typically introduced by confounding variables from the input data. Moreover, the framework is equipped with an intervention training method that not only enhances model training with augmented causal reasoning capabilities but also facilitates dynamic interactions during model inference. These interactions enable users to guide the model's reasoning process, making the Al system more transparent and adaptable in practical scenarios.
We validate the effectiveness of the NCG through extensive experiments on both the ImageNet dataset and a custom causal-related dataset. Our results demonstrate that NCG surpasses existing classification models in terms of both accuracy and robustness. Additionally, we showcase the test-time intervention mechanism of the NCG model, which achieves nearly 95% top-I accuracy on the ImageNet dataset. This capability not only supports post-hoc interpretation but also facilitates intervention processes, allowing for enhanced interactions informed by further human knowledge.
The main contributions of this work are summarized as follows:
We introduce a novel classification paradigm that combines causal inference with neural networks to enhance the interpretability and interactivity of classification models.
We develop a novel algorithm for unbiased causal effect estimation within NCG construction and propose an intervention training method, which allows for test-time intervention on the models.
Extensive experiments are conducted on the ImageNet dataset and a colf-collected causal- related dataset Bird, which demonstrate the rationality and effectiveness of the NCG method. Additionally, we make the datasets and implementations available to the research community facilitate further researct
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RELATED WORKS
Causal Inference. Causal inference frameworks, such as SCM and POM, empower researchers to analyze existing observational data without necessitating extensive interventional studies like
randomized control trials (Rosenbaum & Rubin 1983 Pearl 2000 Bareinboim & Pearl 2014
Bareinboim al. 2015). The SCM defines directed graph representing causal relationships among system variables. This allows for the virtual estimation of interventional experiments using expressions like P(Y]do( ()), where do(.) denotes controlled experiment the real-world variable X. The do-calculus rules have been introduced to transform interventional queries into association expressions that do not include the do operator, achieved by manipulating the causal graph with consideration of conditional independence property (Pearl, 2000). On the other hand, potential outcome model, also known as the Rubin causal model (Rosenbaum Rubin 1983), offers general causal framework for estimating population-Ieve causal effects, often called average treatment effects. In our NCG method, we provide an unbiased estimation formula calculate the causal effects of NCG by integrating both the do-calculus rules from SCM and the propensity score technique from POM.
Classification Model. Recent advances in classification models have been driven by deep neural networks and the application of the softmax operator, which have greatly enhanced the ability to learn discriminative features (Krizhevsky et al. 2012: Szegedy et al. 2013 Zeiler & Fergus 2014
Zhou et al. 2023 Ma et al. 2023). Recent methodologies have implemented causal interventions
on task-level causal graphs, aiming to develop networks that learn without the bias of spurious
correlations (Wang et al. 2021 Yue et al. 2021 Rao et al. 2021; | Qin et al. 2021 Li et al. 2023
Ding et al. 2024). However, these methods exhibit limitations they retain the label independence when performing representation learning, thereby hindering the ability to understand relationships
among labels. In contrast, our NCG framework directly exploits causal information through message propagation on a directed label graph, thus supporting interventional reasoning at the conceptual level instead of the task level.
METHOD
We present the NCG framework, depicted in Figure which comprises two stages: 1) constructing and estimating the neural causal graph; and 2) performing concept construction and concept reasoning. In the following sections, we first present the preliminaries to establish the relationship between SCM and NCG (Section 3.I). We then detail the construction and estimation processes of the NCG (Section 3.2 followed by detailed implementation of the reasoning and training processes built upon it (Section 3.3
3.1 PRELIMINARIES
To effectively utilize the causal information in neural causal graph, we build upon the SCM framework (Pearl 2000). Our SCM is defined as 4-tuple M (U, P(U)). Here, U is a set of exogenous variables that represent unobservable factors external to the model, and C c .,..., C₂J is a set of endogenous variables (concepts) that are determined by the relationships among variables in U C. Each endogenous variable C, depends on its par- ent variables Pa(C,) S C and its associated exogenous variable Uy. The structural functions F == (Sc, Jc,...., Sc,, are deterministic mappings. where each fc, determines C, based on Pa(C,) and U,, i.e. C, t fc, (Pa(C) C2), U,). Finally, P(U) defines the joint probability distribu- tion over the exogenous variables U.
In the context of NCGs, the SCM framework provides a principled way to model the relationships between concepts and their interactions. Specifically, the endogenous variables C represent concept nodes in the NCG, the exogenous variables capture external factors affecting the concepts, and the structural functions F govern how causal information propagates across the concept graph. By leveraging this framework, NCGs enable robust graph reasoning, supporting both interpretable and intervenable classification.
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1
4
Concept Construction
Concept Logits
Concept Reasoning
7.2
Z
Encoder
Concept
Pruposer
Y₁
Y/2
Y3
(a) Neural Causal Graph
(b) Concept Construction and Reasoning
Figure 2: The illustration of our proposed NCG framework. Figure (a) depicts the construction and estimation of the NCG with prior concepts denoted as Z; and posterior concepts as Yj- Different shades of gray the edges represent varying causal effects among the concepts. Figure (b) shows how the model classifies an input X. For simplification, all edges where the sample variable points to each node omitted.
3.2 NEURAL CAUSAL GRAPH CONSTRUCTION AND ESTIMATION
3.2.1 GRAPH CONSTRUCTION
We assume most real-world problems have underlying knowledge structures which can be used to form causal graph. To construct NCGs, we initially preprocess the classification dataset using WordNet (Miller 1995) to extract concepts and causal relationships, in which WordNet is a directed acyclic graph that includes vast number of words and lexical relations. Given classification dataset D = { ((X, y1), (X2, 12)).... (Xm, bm)), where X; E X represents an input, and y: E E (Yr, Y2, Yn) denotes the corresponding label from the label set J' given examples of (X;, 14). We extract all labels Y; from WordNet and consider them as the posterior concepts of NCG. We then identify the ancestor nodes Z {Z,, Zz, .... Zo} of the labels in WordNet prior concepts and retain the corresponding directed edges in WordNet as the node relationships of NCG. To control the size of NCG. we enforce each prior concept to be connected at least one posterior concept within a maximum of five hops. Finally, the NCG for given dataset is formulated as a subgraph = EN, which is extracted from WordNet. Here, A = 2 U Y E (0, 1) both the prior concepts 2 and the posterior concepts (or labels) V while & represents the causal edges connecting these concepts. To facilitate subsequent reasoning processes, all elements A are arranged in topological order, details of which will be discussed in Figure
o+ encompasses
3.2.2 MULTI-LABEL ASSIGNMENT
In traditional multi-class classification, typical visual task assumes that each sample has only one ground-trute -truth label. However, in our NCG paradigm, we transform the task into multi-label classification task better capture concept information. For given example (X, y) in the dataset, we define all ancestor nodes of the label in the NCG as A = {aly S a), where S signifies the reachability relation, indicating the existence of paths from a to y in the directed acyclic graph. Consequently, the label and its ancestor nodes A are considered as the conceptual labels Y (AU C N for the example X. The appendix presents a detailed overview of the labels and their ancestor nodes, as illustrated Figure
3.2.3 CAUSAL INTERVENTION AND ESTIMATION
Assuming that the neural causal graph can be defined as a linear structural equation model (Pearl 2000), estimating the causal effect between nodes becomes challenging task due to the presence of a common confounding factor X. For example, as illustrated in Figure [[c), each concept not only relies on their neighborhood concepts in G. but also primarily depends on a particular sample X. This implies that attempting to estimate the conditional distribution P(C" (C) of an edge C + C' would only describe the correlation rather than the causality, as shown in Figure aa). The sample variable X acts as a confounder for both nodes (C, C"), leading to an open path C X + C", which makes it difficult to ascertain the actual causal effect of C" caused by C. Therefore, the key to estimating the direct causal effect between these nodes lies in mitigating the impact of spurious correlations between C' and C². We aim to address this problem by leveraging the backdoor criteria of the SCM (Pearl 2000). We replace CCC with (CC))do(C)) and block the influence of path C + X C'
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X
X
C'
C'
(a) Local View of NCG
(b) Propensity Score Modeling
Figure 3: Illustration using propensity scores to estimate causal effects in the NCG. Solid arrows indicate direct edges, while dashed arrows signify the presence of at least one unblocked path between X and C Figure (a) shows a local view of NCG where the sample variable X acts as confounder of nodes C' and C². Figure (b) introduces propensity score variable, allowing C' to be directly dependent on a real scalar L == L(X), rather than on the sample variable X.
adjusting the variable X, which is formulated as: P(C")do(C)) = E P(CCIC,))P(X))
where do(-) represents an intervention operation that follows do-calculus (Pear 2000 technique to achieve an unbiased estimation. worth noting that and C' are both binary variables, while X is high dimension variable, which poses difficulties in estimating Equation (1. This challenge also known as the curse of dimensionality problem (Friedman, 1997, Indyk Motwani [998]
Zhang & Zhou 2010), commonly encountered when applying statistical theory to unstructured data. Therefore, reducing the dimension of X becomes crucial to achieve more accurate estimation of Pe
Equation (T) Fortunately, this problem be effectively addressed by introducing propensity score between X and C, as shown in Figure 3(b). This induces a conditional distribution P(C"IC,
that substantially has lower dimension:
P(C))do(C)) = E P(C')C,L)P(L),
(2)
where the equation holds because of the backdoor criterion (Pearl 2000 2012) applied in Figure 3). This NCG estimation scenario establishes a connection between the SCM framework and the POM framework, and Equation ( points out an effective estimation form the causal graph.
3.2.4 CAUSAL EFFECT CALCULATION
To estimate the causal effects of edges in the neural causal graph, we employ two classical methods from the POM framework: Propensity Score Matching (PSM) (Rosenbaum & Rubin 1983 Stuart
2010 and Doubly Robust Learning (DRL) (Bang & Robins 2005 Kang & Schafer 2007 Cao et al.
2009 Both methods compute the average treatment effect (ATE) for each edge, which quantifies the target node (outcome) in the NCG.
causal influence of source node (treatment)
Propensity Score Matching balances treatment and control groups by matching samples with similar
propensity scores (Rosenbaum & Rubin 1983; Stuart 2010), which represent the probability of treatment given background variables. This enables counterfactual inference by comparing outcomes between matched groups
Doubly Robust Learning enhances the estimation by combining inverse probability weighting and regression-batte based methods. This approach ensures robustness against misspecification in either the propensity score or the outcome model, enabling effective estimation of counterfactual outcomes for all samples.
The causal effects computed using PSM and DRL are stored in the NCG adjacency matrix A Raxn Each entry AG represents the average treatment effect from the source node (treatment) the target node j (outcome). If there is no edge between nodes i and Aij is set to zero. This adjacency matrix serves as the foundation for reasoning and training in subsequent stages of the NCG framework. For more details the implementation of PSM and DRL, please refer to Appendix
3.3
MODEL IMPLEMENTATION
3.3.1 CONCEPT CONSTRUCTION
In Figure Dib) the encoder represents a pre-trained network that generates a feature vector = h(X). The concept proposer, denoted as neural network 9(-), takes the feature h IRd input and
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produces concept logits O E Ra for concept reasoning. Here, denotes the number of concept nodes in NCG, and represents the number of heads of NCG. When d = 1, the concept construction process is consistent with the traditional classification paradigm, as each logit directly corresponds to one concept.
3.3.2 CONCEPT REASONING
According the definition of SCM as illustrated in the Section 3.1 for each concept variable Cý C, its reasoning function can be represented as C fc, (X, Pa C2), Uc,). where c, E Rd is a vector determined by its parents C,(C)) and Uc, corresponds to an exogenous variable related to Cj. The function fc, a structural equation that aggregates and updates messages from neighborhoods. Formally, the formulation is shown as follows:
Cj ((g(h(X)) + + Uc,),
where C, Pa(C,) and the subscripts i, are used as index operations that access the corresponding concept nodes, and Aij represents the directed causal weight from C; to Cj. discussed in Section 3.2.4 Uc, is defined as an exogenous variable Uc, ~ ~O, 1). & is a update function, typically set as an identical mapping function in the topological order of the NCG for each node, we can transform the initial concept logits O Raxd into updated concept logits O' Rndd
form a linear model. Finally, by applying the reasoning
3.3.3 LOss CALCULATION
After the process of concept reasoning, we calculate the loss for optimization by using the binary cross entropy as follows:
{ c - [Y: Y, + (1-Y;) log(1 Y.) Y, ==((OO...
where Y; E f0, 1} represents the ground-truth label of the 7th node in NCG, and n denotes the total number of concept nodes. w; R is a learnable scaled weight of the jth node that initialized as 1 //d for each head. It transforms the updated concept logits to a score, and a is the sigmoid function that produces a predicted probability for each concept node.
3.3.4 INTERVENTION TRAINING METHOD
To enable the model to perform test-time interventions by answering questions like "What would be we do Z", commonly referred to as "What if" question (Pearl 2000), we propose the intervention training method that equips the model to reason based intervened concepts. This approach is designed to enhance the test-time intervention ability by aligning the training and testing distributions, ensuring robustness under test-time interventions. Inspired by the principles of structural causal models, this approach simulates the effects of the do(-) operator during training, isolating the influence of intervened nodes by eliminating their dependence on parent nodes. Specifically, during each training iteration, a subset of prior concepts is randomly intervened with an intervention rate p 0.15, and their concept logits are fixed using the formula (2Y, 1) x v. Y, E {0, 1) represents the corresponding multi-label value, and is an empirically determined confidence value set to 5. The sigmoid output of or 5 reflects high certainty about the node's intervention status.
This method injects reasoning capabilities into the model by explicitly forcing it to learn the dynamics among concepts while addressing "What if" questions. By simulating test-time scenarios, the model can be conditioned to predict posterior concepts based on both inferred and intervened prior concepts. An overview of the procedure for intervention training detailed Algorithm 2
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. Two datasets are used in this study: Bird and ImageNet (Deng et al., 2009). The Bird dataset is self-collected and more interpretable subset of the larger ImageNet dataset. Table

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Table The statisties of two datasets, including the number of prior and posterior concepts, and the size of training set and testing set.
Dataset
Prior
Posterior
Training set Testing Set
Bird
9
11,700
450
ImageNet 1,357
1,000
1,281,167
50,000
Table 2: The overall performance comparison of various methods on the Bird and ImageNet datasets. Each row represents specific model performed. The best performance for Acc and F1 is highlighted in bold. The baseline model for t-test is Multi-class. (Section 4.2
Dataset
Backbone
Model
Acc
Std
p-value
F1
Std
p-value
Multi-class
90.89
0.92
90.81
0.95
Multi-label
91.33
1.28
0.5897
91.32
1.26
0.5383
ResNet50
CBM
90.22
1.10
0.3814
90.06
1.21
0.3585
NCG (PSM)
92.31
0.36
0.0207
92.29
0.37
0.0205
NCG (DRL)
93.42
0.11
0.0006
93.41
0.11
0.0006
Bird
Multi-class
93.33
0.63
93.31
0.67
Multi-label
93.16
0.74
0.7349
93.15
0.82
0.7713
CLIP
CBM
90.67
1.10
0.0030
90.64
1.22
0.0049
NCG (PSM)
94.22
0.20
0.0270
94.22
0.20
0.0315
NCG (DRL)
94.49
0.17
0.0074
94.53
0.17
0.0081
Multi-class
73.08
0.09
72.35
0.11
Multi-label
73.23
0.05
0.1882
72.52
0.06
0,2256
ResNet50
CBM
73.22
0.55
0.7545
72.78
0.70
0.4736
NCG (PSM)
73.75
0.02
0.0035
73.35
0.11
0.0046
NCG (DRL)
73.73
0.04
0.0045
73.24
0.03
0.0033
ImageNet
Multi-class
83.49
0.06
82.97
0.06
Multi-label
83.58
0.20
0.5744
83.05
0.16
0.5466
CLIP
CBM
83.32
0.79
0.7847
82.90
0.98
0.9290
NCG (PSM)
84.44
0.12
0.0029
83.98
0.14
0.0032
NCG (DRL)
83.98
0.08
0.0098
83.35
0.14
0.0508
provides the statistics for both datasets. Given the limited size of the Bird dataset, multiple repetitions of experiments can be conducted to ensure the reliability the model performance. NCGs for both two datasets are constructed using the previously described process, and graphical overview for the Bird dataset is provided in Figure
Evaluation Metries. The performance of the model is evaluated using two widely applied multi- classification metrics (Pedregosa et al.) 201T): accuracy (Acc) and macro-F1 score (F1). Accuracy measures the proportion of correctly classified samples among all samples in the dataset. It provides a general overview of the model's overall performance. Macro-F1 score calculates the harmonic mean of precision and recall for each class, and then takes the average balanced assessment of the model performance across all classes. Note that for the Bird dataset and ImageNet dataset, we repeat each experiment 5 times and times, respectively, and the average results are reported. We calculate standard deviation (Std) to indicate the variation in results of Acc and F1. P-values are also provided using a two-sample t-test, with p-values less than 0.05 indicating statistically significant difference compared to the baseline model in different experiments.
all classes. It gives
Baselines. Our NCG framework can be viewed as a plug-in module built upon traditional classification methods, so we choose multi-class and multi-label classification settings baselines. The multi-class baselines include training the model using only posterior concepts, while the multi-label baseline involve using both the prior and posterior concepts for model optimization. Additionally, Concept Bottleneck Model (CBM) (Koh et al., 2020> as another bascline. The CBM predicts prior concepts through bottleneck layer, and then uses these concepts to predict the label. This design allows for intervention by editing prior concept values and propagating these changes to the final posterior concept values.
utilize
Implementation Details. To demonstrate the effectiveness of our proposed framework, we build NCG model using frozen pre-trained backbones, ResNet50 (He et al. 2016) and VIT from
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Table 3: Experimental results of the NCG framework using different causal weights on the Bird dataset. The best performance for Acc and FI is highlighted in bold. The baseline model for t-test is One. (Section
Backbone
Weight
Acc
Std
p-value
F1
Std
p-value
One
91.07
0.50
91.21
0,40
Random
90.85
1.80
0.8179
90.81
1.85
0.681
Zero
91.96
0.95
0.1357
91.92
1.00
0.2237
ResNet50
Learn
91.87
0.23
0.0192
91.80
0.23
0.0329
PSM
92.31
0.36
0.0037
92.29
0.37
0.0044
DRL
93.42
0.11
< 0.0001
93.41
0.11
< 0,0001
One
92.76
0.23
93.02
0.20
Random
92.51
1.00
0.6482
92.49
0.99
0.3226
Zero
93.69
0.30
0.0011
93.69
0.29
0.0053
CLIP
Learn
94.18
0.26
0.0001
94.19
0.25
0.0001
PSM
94.22
0.20
0.0270
94.22
0.20
< 0.0001
DRL
94.49
0.17
0.0001
94.53
0.17
0.0001
OpenCLIP-H-I4 Radford et 2021). Recognizing the disparity complexity between the two datasets, we employ linear layer perceptron as the concept proposer for the Bird dataset and and two-layer perceptron (with intermediate channels equal to input channels) for the ImageNet dataset. For the ImageNet dataset, we also utilize an eight-head NCG (d = 8) and set the update function ø as three-layer perceptron with 64 intermediate channels, incorporating tanh activation functions in the first two layers to regulate the scope of the final output.
Our framework implemented based on PyTorch and DGI and all models are trained with mini- batch size of 5 512 machine equipped with four Nvidia-30908 GPUs. We AdamW (Kingmal Ba 2014; Loshchilov & Hutter 2017) as the optimizer with a learning rate of 0.01 for the Bird datasct and 0.001 for the ImageNet dataset. We train the models for 50 epochs for the Bird dataset and 3 epochs for the ImageNet dataset without any data augmentation.
and
4.2 OVERALL PERFORMANCE COMPARISON
To evaluate the effectiveness of our proposed method, conduct several experiments using two different backbones on the two datasets to compare the performance of our proposed framework against baseline approaches.
Table abble[ddmoostrrtees that the two proposed NCGs outperform other baselines on different datasets and backbone models. We have the following observations: 1) NCGs outperform Multi-class and Multi-label methods. We argue that this is because NCG models learn the causal relationships among concepts, which gives them better generalization ability: 2) The performance of NCGs is significantly better than that of CBM. We attribute this to the correct concept structure introduced by the causal graph, whereas CBM only separates the prior concepts and posterior concepts without explicitly modeling their causal relationships; 3) Interestingly, it is important note that NCG models improve the performance of ResNet50 (NCG vs. Multi-class), a model that already pre-trained on ImageNet. This indicates that valuable information for classification is derived from the NCG's causal structure and weights.
4.3 EFFECT OF CAUSAL WEIGHTS
To investigate the importance of causal weights, we devise six variants of the NCG model with different causal weight implementations, including PSM, DRL, Learn, One, Random and Zero. PSM and DRL are unbiased estimation methods based on the potential outcome model. One and Zero methods refer to the weights being set as a constant one or zero for each edge, respectively. Random denotes randomized weight value from zero to one, while Learn implies that the weight is initialized one and is learnable during training.
https://pytorchh /gp//ggintttggttteeee openclip/
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Table 4: The experimental results of using different components based on ResNet50 and CLIP backbones, Note that IT and LSW are the abbreviation of Intervention Training and Learnable Scaled Weight, respectively. The best performance for Ace and F1 is highlighted in bold. The baseline model for t-test is w/o IT and LSW. (Section 4.4)
Components
Acc
F1
Backbone
Model
IT
LSW
Value
Std
p-value
Value
Std
p-value
92.31
0.36
0.0216
92.29
0.37
0.0296
91.29
0.62
0.0952
91.18
0.60
0.1112
NCG (PSM)
90.89
1.53
0.2237
90.86
1.57
0.2083
89.11
0.36
88.75
0.37
ResNer50
93.42
0.11
0.0042
93.41
0.11
0.0067
92.09
0.30
0.0470
92.05
0.29
0,0631
NCG (DRL)
91.38
0.88
0.2111
91.41
0.89
0.2093
90.09
1.68
90.00
1.88
94.22
0.20
0.0085
94.22
0.20
0.0079
93.29
0.41
0.3728
93.27
0,42
0.3859
NCG (PSM)
93.47
0.33
0.1924
93.46
0.33
0.1901
92.89
0.20
92.88
0.20
CLIP
94.49
0.17
0.0088
94.53
0.17
0.0073
94.44
0.14
0.0100
94.41
0.15
0,0106
NCG (DRL)
93.69
0.27
0.1535
93.70
0.26
0,1400
92.98
0.17
92.96
0.17
As shown in Table 3 we have three observations: 1) The performance of PSM and DRL methods is superior to that of other methods on both backbone models, indicating that the estimated causal weight captures the relationships among concepts. 2) Methods with constant weights, such One, Random and Zero, suffer from incorrect weight assignments and exhibit higher errors on both backbones. This emphasizes that accurate causal weight estimation crucial for optimization and reasoning of NCG. 3) The Learn method demonstrates moderate success with CLIP but is inadequate with ResNet50, exposing training instability and the inherent difficulty in directly learning causal weight between concepts. This further validates the effectiveness of our proposed PSM and DRL estimation methods.
4.4 ABLATION STUDIES
We conduct ablation studies on our NCG framework to explore the impact of intervention training and learnable scaled weight, which are specially designed for optimization and reasoning within our NCG framework. As shown in Table we have the following observations: 1) Utilizing both intervention training and learnable scaled weight significantly enhances the NCG framework's reasoning ability across two backbones and two distinct causal weights. Without these elements, the performance decreases considerably, validating the efficacy of these design features. 2) The intervention training component alone greatly improves the reasoning ability of the NCG model, evidenced by marked improvement of nearly 2% over the worst model. Such improvement can be attributed to causal intervention, which compels the model to address "What If" queries within the NCG, thereby fostering reasoning skills among various concepts. Furthermore, the NCG can be treated as a world model about dataset, encompassing causal knowledge about different concepts. Therefore, intervening on the world model creates many -iitt---iitiittiion examples, which can be viewed as data augmentation method and thus facilitating the learning of NCG's ' generalization ability. 3) Compared to models lacking both components, the incorporation of learnable scaled weight alone yields significant improvement. It is because those prior concepts that posterior concepts may have much larger updated logits because their values are accumulated from their ancestors, which results in the label imbalance problem. Therefore, the learnable scaled weight can reweight different updated logits and achieve a more balanced loss.
4.5 TEST-TIME INTERVENTION
We propose a rest-time intervention experiment to harness the powerful potential of human-Al collaboration in our model. By enabling intervention prior concepts, human users can interactively

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w/ Intervention Training
wo Intervention Training
0.9
0.9
0.8
0.8
Backbone
0.7
Backbone
0.7
CLIP
Accuracy
0.6
CLIP
T 2.6
ResNet50
0.5
ResNet50
0.5
Causal Effect
0.4
Causal Effect
0.4
CBM
0.3
CBM
0.3
DRL
0.1
0.2
DRL
0.1
0.2
PSM
0.0
0.0
200
400
600
800
1000
1200
400
800 1000 1200 1400
Node Number of Intervention
Node Number of Intervention
Figure 4: The test-time intervention accuracy of various models on the ImageNet dataset. (Section 4.5)
guide the model's behavior during testing. Through a series of controlled experiments, we explore how incremental interventions across different segments of prior concepts influence the accuracy of posterior concepts. Specifically, we investigate the intervention probability P() do 2')). where Z' is a subset of prior concepts Z. To understand the nuanced effects of human intervention and reduce the computational demands during test-time intervention, we divide the prior concepts )= incrementally intervening the first 1 segments Z' {2 using the corresponding labels, and evaluate the accuracy of posterior concept on the ImageNet testing set. The intervention values are applied consistently with the intervention training method.
Z c C into 25 equal segments ( Following these segments, conduct 25 experiments by
As shown in Figure 4 for the models that perform the intervention training, we observe a significant increase in the accuracy of PSM and DRL as the number of intervention nodes increases, reaching peak performance at approximately 95% top-1 accuracy. This highlights the effective human-Al Al interaction ability of NCG. Conversely, for the models without intervention training, we find that only the PSM model successfully learns the intervention rule on the CLIP model, indicating the challenge of developing reasoning ability from scratch without guidance. Additionally, we observe that the CBM model perform worse even when trained with the intervention method, demonstrating the importance of causal structure in reasoning. Interestingly, DRL + CLIP often improves performance with an increasing number of intervention nodes, although there is a noticeable drop in accuracy after a certain point (For details, please refer to Section A.8). This because our NCG models, even without interventional training, are capable of learning causal dynamics and performing interventions inherently based on the underlying causal structure. Therefore, we believe the observed decline in accuracy for DRL CLIP is an anticipated outcome.
In conclusion, this experiment demonstrates that our NCG method provides post-hoc interpretability, allowing users to understand how the reasoning process leads to specific predictions for posterior concepts. This aligns with existing research on interpretability in graph neural networks (Yuan et al.. 2022). Additionally, our method extends beyond post-hoc analysis by enabling users to actively influence the model's 's reasoning during testing through test-time interventions.
CONCLUSIONS
We have developed novel classification paradigm, neural causal graph, designed facilitate interpretable and intervenable reasoning with enhanced generalization capabilities. NCG incorporates structural causal model into the classification framework, enabling the disentanglement of complex knowledge representations and structures. This integration allows for precise reasoning based on concept information derived directly from an underlying causal graph, enhancing the model's s ability to understand and interpret the data. Additionally, we developed an innovative intervention training and testing approach that enhances the NCG model's reasoning abilities and fosters collaboration between humans and AI. This approach involves posing "what if"" questions, which challenge the model to consider intervention scenarios and adapt its predictions in the training stage, thereby enhancing its ability to handle interventions at test time. Finally, our experimental results demonstrate that the NCG approach has the potential to achieve interpretable and intervenable reasoning capabilities in classification tasks.
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ACKNOWLEDGMENTS
This work is supported by the Natural Science Foundation of Hunan Province (No. 20221130159 and No. 2023JJ20013), the National Natural Science Foundation of China (No. 61802121), and the Fundamental Research Funds for the Central Universities.
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Algorithm Pseudo code for constructing the causal graph.
Input:
dataset = {(X1,, v1); (X2, .2), (Xm, Wm)), directed acyclie graph GWordNet of Word-
Net.
Output: causal graph 9 = & for the given dataset, multilabel y of all examples, causal weight matrix A for the causal graph.
+ G-GrphhhCoo ---GrraaaConstit Construction(D) ( GWordNet)
= .y...., Fm] for each edge (C2, Cj) in & do
f MultilabeLAssiinmmeee
9)
A.j < Causal Efffeet Calculation(C;, Cj. (X2, )
end for return G.Y.A
A
APPENDIX
This appendix is organized as follows:
Section
A.
presents algorithms for constructing and training the NCG.
Section
A.2 2
offers overview of the causal graph for the Bird dataset.
describes the detailed implementation of propensity score matching (PSM) and doubly ro bus learning (DRL) for causal effect calculation in NCG.
Section
A.3 3
Section
A 4
delves into the detailed reasoning process of NCG.
Section
illustrates the intervention process specific nodes of the NCG with examples.
Section A. 6
examines the effect of different intervention rates during the training phase
model per performance.
Section A.7 details the test-time intervention experiment with human annotators to demon- strate interpretabiiity and interactive capabilities of NCG.
Section A.8 analyzes the performance drop in DRL+CLIP without intervention training and explains its causes.
Section A.9
shows case study that demonstrates the concept reasoning process of NCG.
provides an explanation of the rationale behind using top-level prior concepts as shared labels, clarifying their role in maintaining implementation simplicity and facilitating information propagation within the causal graph.
Section A.10
A.1 ALGORITHMS
This section provides two algorithms using pseudo code for detailing the construction the causal graph and the training procedure for the NCG network. Detailed implementations of Algorithm T and Algorithm2) can be found in the Method section the main manuscript.
can
A.2 DATASET VISUALIZATION
We provide the concept and structure overview of the Bird Dataset. Since the graph of the ImageNet dataset is huge, we only illustrate the graph of the Bird dataset as schematic diagram. As depicted in Figure 5 the Bird Dataset contains a total of 25 nodes, with 16 nodes representing prior concepts and nodes representing posterior concepts. Each posterior concept has unique set of ancestors, comprised of several prior concepts that share common features with the posterior concepts.
A.3 CAUSAL EFFECT CALCULATION: DETAILED IMPLEMENTATION
This section provides the implementation details of the Propensity Score Matching (PSM) (Rosen baum & Rubin 1983 Stuart 2010) and Doubly Robust Learning (DRL) methods (Bang
& Robins Cao et al. 2009) used to estimate causal effects for edges the NCG.
2005: Kang & Schafer 2007
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Algorithm 2 Pseudo code for training the NCG network.
Input:
multilabel dataset D = {X&, Y causal graph g = (N,E) for the given dataset, pre- trained encoder k(-), intervention _rate p. confidence value
Output: concept proposer 9(-). NCG network f(-), learnable scaled weights w. optimizer initialize imitialize.optmmizen((.. f])
for randomly sample batch B {Xx= YR
16s from D do
Aro < sample intervennion /owooss,nodddd/.nnoo
Y - multihot ( Y 11)
C < Concept Construction Xxx 14:; [g. h))
for each node Cj in lopological_sort/ do
if Cj in MnAoo rdo then
C' < (2x x [::]] 1) x
else
C' f- Concept Reasoning CE: jl. Pa(C,)
end if
C f Cj note: C =- C|:.]]
end for
loss + Loss Calculation(C', w, Y) optimizer.7ero grrd() loss. bask.back optimizer.step()
end for
animal
food
chordate
meat
vertebrate
game
gallinaccous bird
parrot
game bird
african_
macaw
cockatoo
grouse
phasianid
wildfowl
sulphur-crested cockatoo
ptarmigan
rufted grouse
prairic chicken
pheasont
quail
partridge
peafowl
peacock
Figure 5; The illustration of concept structure of the Bird dataset.
A.3.1 PROPENSITY SCORE MATCHING (PSM)
PSM estimates the causal effect of an edge in the NCG by comparing the outcomes of treatment and control groups with similar propensity scores. In context, the source node of the edge represents the treatment C, while the target node represents the outcome C'. The steps are as follows:
1. Propensity Score Estimation. We model the propensity score (Rosenbaum & Rubin 1983 Stuart 2010), defined as P(C (the likelihood of treatment C given background variables X), using logistic regression. Specifically, for dataset of feature-concept pairs {(h,, C.) where h; R
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represents the feature vector of input X; and C, {0, 1) is the treatment indicator, train logistic regression model with linear weight w:
P(C = 1|x)
log - P = 1(X)
h,
This yields the propensity score estimator Ż(x) - P(C = 1|x) for all samples.
Matching. Using the estimated propensity scores, we perform nearest neighbor matching (Rosen baum & Rubin 1983 Stuart 2010) to pair each treated sample (C= = 1) with control sample (C = 0)
that have the closest propensity scores. This minimizes the bias caused by confounding variables.
3. Causal Effect Estimation. The average treatment effect (ATE) is computed as the mean difference
in outcomes between matched treatment and control groups:
ATE (C = CC 0)],
(5)
where (C = 1) and C²(C = 0) are the potential outcomes under treatment and control conditions, respectively. These processes are calculated for all edges in the NCG.
A.3.2 DOUBLY ROBUST LEARNING (DRL)
DRL provides robust alternative PSM by combining inverse probability weighting with regression- based outcome modeling. This ensures reliable estimation of causal effects, even when one of the models (propensity score or outcome) is misspecified. The steps are as follows:
1. Outcome and Propensity Models. DRL involves two predictive tasks: (i) estimating the propensity score f(X). in PSM, and (ii) modeling the conditional outcome given the treatment and background variables, E[["|Xx 1] and E[C" = =]. We use logistic regression for both tasks to maintain simplicity and consistency:
(x,)
= h.), CE (0,1),
where (.) is the sigmoid function.
Treatment Effect Estimation. The ATE computed as:
C,C. - -Ic-))((x)). + +01(x,))
ATE
i(Xi)
(1- C.) -1-C))CC-Qo(X)) (o(x)) + @o(Xx))
(6)
1 L(x,)
where (x,,) and (X,) are the predicted outcomes for treatment and control groups, respectively, and i(x,) is the estimated propensity score. m is the sample number of dataset.
A.4 CONCEPT REASONING
In the Section of Concept Reasoning of the main paper, we discussed the reasoning function of NCG. As depicted in Figure 66 every node in NCG has been assigned index that corresponds to the topological order. During the reasoning process, we apply the reasoning function on each node in order of their indexes, which ultimately produces complete message propagation process.
A.S INTERVENTION ILLUSTRATION
In the Section of Intervention Training Method of the main paper, we discussed how NCG can be intervened to improve its reasoning capabilities. Specifically, as shown Figure we fix the concept logits of the 10th and 1 1tth nodes (parrot and game bird), which cut off the influence from their ancestors. This intervention process can be thought of as contextual prompt: we assume that the bird in the image is more likely to be parrot than game bird, what exact type of bird could it possibly be? By answering such questions, NCG can better understand the causal relationships between different concepts and make more accurate predictions.
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15
12
11
16
5
8
24
13
17
20
21
2
4
7
10
22
25
18
23
19
Figure 6: The nodes of NCG on the Bird dataset have been sorted topologically, with each node assigned an index according to this sorting.
14
12
3
11
16
5
6
8
24
13
17
20
21
2
10
22
25
18
23
19
Figure 7: The intervenetion operation on two nodes of NCG.
A.6 THE INTERVENTION RATE ON TRAINING TIME
Intervention training is crucial technique in the NCG method, and it is necessary to study the impact of different intervention rates during the training phase. The results from Figure X indicate two important observations.
Firstly, we notice that the performance of PSM and DRL methods gradually increases with the increasing of intervention rate. The highest performance is observed at the 15% intervention rate, after which the performance starts to decline. This can attributed to fact that with too many prior concept nodes being fixed, the optimization process is being underly constrained, leading to poor generalization performance. Secondly, we find that when intervention training is applied with appropriate intervention rates, PSM and DRL methods show consistent improvement in performance compared to the CBM method. This suggests that the NCG approach effectively captures the relationship between different concepts, while the CBM method fails learn knowledge because it lacks of the concept structure that makes the intervention become noise.
A.7 TEST- TIME INTERVENTION WITH HUMAN ANNOTATORS
To demonstrate the interpretability of our model, i.c., allowing humans to understand and participate in the reasoning process, we conducted a test-time intervention experiment involving real human annotators. The goal of this experiment is to evaluate whether human interventions could improve model predictions by leveraging the interpretable structure of the neural causal graph. Below, detail the experimental setup, process, and results.
Sample Selection:
We selected 198 testing samples from the Bird dataset, covering posterior concepts, ensure diversity in the evaluation.
Intervention Process:
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Performanc wir.. Intervention
0.94
Accuraty (%)
0.92
0.91
Backbone
Res/iet5 50
Causal Method
0.89
PSM
DRL
0.88
CBM
0.1
0.2
0.4
0.5
Intervention
Figure 8: The performance of NCG with different intervention rate (0, 0.05, 0010, 0.15, 0.25, 0.50) on the Bird dataset.
Table 5: Effect of Human Interventions on Model Accuracy. "H" is the abbreviation for "Human".
Pre-Intervention
H1
H2
H3
H4
H5
Human Avg,
Correct Predictions Accuracy (%)
178
191
188
186
191
189
189
89.9
96.5
94.9
93.9
96.5
95.5
95.5
1. Each image was classified by the NCG (PSM) model, which generated predicted These
probabilities for all concepts in the graph. These predictions were presented in format similar Figure 9 in the main paper.
Human annotators were shown the predicted probability graph alongside the original image. Annotators were asked to provide interventions by labeling one more prior concepts as either (absent) or (present), reflecting their judgment of what the image represents.
The annotated prior concepts were used update the graph, and the NCG (PSM) model performed reasoning based on the updated values and the input image.
The accuracy of the model's predictions before and after user interven- tion recorded to measure the effectiveness of the human-guided updates.
Evaluation Metric:
We conducted this study with five human annotators who interacted with the model during the test-time intervention process. The results, summarized in Table show significant improvement model accuracy after human interventions.
The results indicate that human interventions significantly improved the model's prediction accuracy, increasing from 89.9% pre-intervention to an average of 95.5% across the five annotators. This demonstrates that the NCG framework enables humans to interact effectively with the model by interpreting and adjusting its causal reasoning process. The ability to intervene dynamically highlights the interpretability and flexibility of our approach, supporting sophisticated post-hoc analysis and facilitating human-Al collaboration.
This experiment validates the claim that method yields interpretable concepts and arranges them manner that is understandable and actionable for human users.
A.8 PERFORMANCE DROP FOR DRL+CLIP W/O INTERVENTION TRAINING
As shown Figure the accuracy drop after around 1300 nodes likely arises because, without intervention training, the model can not reliably learn causal dependencies of graph, particularly as the number nodes grows. There are three main reasons that, an unexpected combination. led to this phenomenon when the model lacks interventional training. Note that our proposed NCG models with intervention training do not suffer from this issue, showing the necessity of our proposed intervention training process.
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15: 0%
67%
MMS
130%
1:0%
24.
1305
9%
805
1091 (10%
22.0%
(a) Before graph reasoning. The 18th node with blue color represents the wrong prodiction.
(b) After graph reasoning. The 14th node with green color indicates the correct prediction.
Figure 9: An example is selected from the Bird dataset to visualize the effectiveness of NCG. The first figure shows the sigmoid probabilities of the concept logits obtained from the concept proposer. The second figure illustrates the sigmoid probabilities of the updated concept logits after performing concept reasoning. In both figures, the yellow nodes represent the prior concepts of NCG, while the other color nodes represent the posterior concepts.
1. Positive Inductive Bias in the Causal Graph: The model inherently interprets positive logits in the formula Cj ((g(h(X)) E. Ar,c, + Uc,) an indication of the presence of certain concepts in the images. This is reasonable assumption given the structure of our causal graph, which has an inductive bias toward positive causal relationships. As detailed in the paper, the causal effect weights are typically estimated with 1 representing the presence of a concept and 0 representing its absence. This means that the estimated causal edges generally have positive weights, leading to an increase in the logits of child nodes when the logits of the corresponding parent concepts increase.
Negative Causal Weights in DRL Estimation: Although most of the estimated edge weights are positive, there are few exceptions where the causal weights are negative. This occurs because the DRL method uses a regression- based estimator to calculate causal effects, and in some cases, this results in negative weight. When these negative weights exist, to increase the logits of their child nodes, the model must reduce the logits of parent concepts.
3. Intervention on the Last 100 Prior Concepts Close to Posterior Concepts: We observed that the last 100 prior concepts in the causal graph are most closely related to the posterior concepts within one-hop. During the intervention, extreme cases where the causal weights are negative, applying a -5 intervention to the prior concepts was mistakenly interpreted by the model as increase in the corresponding posterior concept logits. This misinterpretation possibly leads to wrong classification results, thus causing a sudden and significant drop in accuracy.
used values of -5 and 5 to manipulate the prior concepts. In
These factors combined to produce the drastic drop in model performance after the initial 1300 effective node interventions. Note that this situation arises because the model did not perform intervention training, and the fact that the model was able to improve performance on the first 1300 nodes is already a noteworthy result. Conversely, models that have undergone proper intervention training do not suffer from this issue, demonstrating the effectiveness of our approach.
A.9 CASE STUDY
As demonstrated in prior experiments, our model leverages NCG to enhance classification capabilities beyond the multi-class and multi-label baseline. To clearly illustrate this improvement, we analyze classification variations before and after concept reasoning with an NCG model, as depicted in Figure Initially, the model incorrectly predicts the bird as "quail" (node 18: 88%). However. after applying concept reasoning, the prediction shifts to "ptarmigan" (node 14: 19%). This adjustment in- dicates that NCG effectively refines predictions by leveraging the interconnected concept information within the graph. On closer inspection of the probability of the prior concepts "grouse" (node 12: 93%) and "phasianid" (node 13: 0%) before concept reasoning, we can observe that this instance is more likely to be "grouse" than a "phasianid". Considering ptarmigans belong to the grouse family, the accurate post-reasoning prediction is "ptarmigan", This case study demonstrates NCG's capacity to integrate all relevant concepts and causal relationships. offering a comprehensive understanding of the example and showcasing effective integration neural networks with causal inference for interpretable and intervenable classification.
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A.10 ROLE OF TOP-LEVEL PRIOR CONCEPTS AS SHARED LABELS
As explained Section 3.2.2 WordNet's structure ensures that examples with different labels are assigned distinct prior concepts, benefiting the training process by capturing meaningful relationships within the graph. However, in the Bird dataset, the top-level concept "animal" is labeled as "1" for all examples because all birds inherently belong the "animal" category. This uniformity provides limited discriminative information for distinguishing between classes, which raises questions about its utility in the classification process.
While this might seem counterintuitive, it reflects design choice aimed at maintaining simplicity and consistency the implementation described in Section 3.2.2 Importantly, only a very small number of top-level concepts, such as "animal" are shared by all examples in the dataset. Furthermore, assigning "I" to these concepts ensures that they can propagate information to subsequent nodes in the causal graph. aligning with the hierarchical structure of the real world. This approach allows the model to represent and utilize inherent structures without introducing redundancy or complex designs.
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Reviews:
[{"review_id": "ZNNN4h0ONf", "reviewer": "Reviewer_bMLv", "summary": "This paper combines the causal inference frameworks of structural causal models and potential outcomes models, using neural network implementations, to provide better transparency and interactive reasoning capabilities in classification settings. Estimation of causal effects (ATEs) to build the underlying graph is is done using propensity score matching and doubly robust learning. Compared to naive utilization of neural networks for classification, this approach gives a more inherently interpretable model for classification by explicitly modeling underlying causal relationships. The authors demonstrate their method on the ImageNet and Bird datasets and show improved performance as well as improved interpretability and human intervene-ability (via test-time intervention experiment) over certain neural network baseline models. They argue it is the explicit modeling and tweaking of underlying causal structure that gives accuracy improvements, with some ablations as evidence.", "questions": "Is there anything systematic that you can comment on or hypothesize about in Section 4.2 about DRL doing better on Bird vs. PSM doing better on ImageNet performance in Table 2?\n\nYou are space constrained, but since the intervention training method is an important contribution, I wonder if it could get a little more space in the main text.\n\nRegarding your explanation of the accuracy drop for DRL+CLIP w/o intervention training: are you saying that the method reaches a point (high number of nodes) after which it can’t learn the underlying structure (w/o intervention training) and therefore reason appropriately (“get the right answer”)? Are there metrics other than accuracy that could demonstrate the difference in underlying reasoning (even just at the point on the graph, ~1300 nodes for CLIP+DRL, between the two graphs) and make your claim stronger?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Very clear intro and stated contributions. Work is original and well-motivated. Results feel complete and experiments are convincing (e.g. with ablations showing the importance of both Intervention Training and Learnable Scaled Weight). As a reader, most of my immediate/obvious questions are answered in the paper and/or the Appendix.\n\nStrong conceptual figures; I am familiar with traditional causal inference frameworks and appreciated the concise and relevant presentation here. Tables and graphs are appropriately used; the one on ablations is especially clear. The Appendix provides clear pseudocode and more concrete examples and visualizations of the authors’ method and experiments – relevant info to better understand the model and application concretely.\n\nRepresents a solid and clear contribution and advance for a conference paper work. Above acceptance threshold.", "weaknesses": "I have no major issues with this paper.\n\nSmall typo: 310/311 intervention is spelled wrong.\n\nMore of a leading question: is the prior work complete? It's a relatively small section of the Intro, and I wonder if there are previous attempts to integrate causal inference frameworks in NNs (maybe not in interpretability specifically) that are important to acknowledge and relevant to cite. I.e. previous work on modeling causality with GNNs? Your paper does not obviously acknowledge that causal reasoning on graphs implemented as NNs is an established concept. You say \"Unlike conventional methods that rely on correlation-based learning, NCG constructs a causal graph...\" but GNNs attempting to model causality aren't mentioned.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "v59LrstbTE", "reviewer": "Reviewer_sJC6", "summary": "This paper proposes a new interpretable and intervenable neural network. The main idea of the proposed models is to incorporate a causal graph among latent concepts and target labels into a neural network. To train such a model, the authors propose calculating the causal effects between concepts and labels by propensity score matching and doubly robust learning. Its prediction of each concept or label is modeled by the linear combination of the estimated causal effects of the parent concepts in a causal graph, as well as the output of the encoder in a pre-training network. The authors also propose an intervention training technique that generates counterfactual samples by intervening in selected training samples according to the causal graph. Experimental results demonstrated that the proposed method attained higher accuracy than the existing baselines, including the concept bottleneck model.", "questions": "- Q1. Concerning W1, is my understanding of the proposed method correct?\n- Q2. Concerning W2, from what perspective do the authors claim that the proposed model is interpretable?\n- Q3. What was the purpose of the experiments in Section 4.5? If the test samples were intervened and their concepts and labels were perturbed according to a causal graph, it seems natural that the accuracy of the proposed method increased and that of CBM decreased since the former was trained with the causal graph and the latter did not. Can this experiment be considered fair?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- S1. The authors proposed a new concept bottleneck model by incorporating a causal graph that represents causal relationships between latent concepts and target labels. I think it is interesting and useful to generate concepts corresponding to the labels and their causal directions using WordNet. \n- S2. The proposed method slightly improved the accuracy and F1 score on the Bird and ImageNet datasets compared to the baselines.", "weaknesses": "- W1. It was difficult for me to understand the whole picture of the proposed method. Maybe this is because this paper is structured to describe the proposed method in a bottom-up manner. I believe describing the proposed method in a top-down manner, that is, first giving an abstracted model of the proposed model and then describing its formulation of each detail, may help readers understand the proposed method. In addition, while the authors introduce several unique techniques (e.g., causal effect calculation, concept reasoning, and intervention training), it was unclear to me what purpose these techniques are introduced for and what observation or motivation they are based on. If my understanding is correct, the proposed method can be regarded as an extension of the concept bottleneck models incorporating causal relationships between labels and concepts. I think this paper may be improved by organizing it following the original paper on the concept bottleneck models [Koh et al. 2020]. \n- W2. This paper claims that the proposed method is interpretable. However, I could not find any discussion or evidence demonstrating the interpretability of the proposed method. \n- W3. I found some confusing notational errors. For example, while $n$ represents the total number of labels in Line 153, it denotes the total number of nodes in a causal graph in Line 268. Also, in Equation (5), $n$ appears to represent the total number of training samples. Furthermore, while $C$ is defined as a binary variable in Line 203, it denotes the set of concept variables in Line 275. In addition, I think $w_{ij}$ in Equation (6) is correctly $A_{ij}$. Since such notational errors prevent readers from understanding the proposed method, I believe the mathematical part of this paper should be corrected entirely.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wmAZBaceiK", "reviewer": "Reviewer_ytQg", "summary": "The paper proposes a classification framework that integrates causal inference within neural networks to enhance interpretability and facilitate human-guided interventions. Experiments are conducted to see how performance compares against baselines such as concept-bottleneck models, then the effect of causal weights, ablation studies, and test time intervention capability.\n\nOverall, the results seem to show that the performance is promising and test time intervention feasible.", "questions": "* Why did the regular classifiers in Table 2 perform worse than your method? This doesn't make sense to me as you are incorporating more constraints, did the causal reasoning actually result in better results?\n* Did you add multi-lables for each datapoint by using WordNet? If so, then all images of e.g. Bird1 have all the same concept labels in every example you train on? Isn't that just making the classification problem more convoluted? Surely each image of Bird1 should have varying concept labels to truly benefit the training process and your method? Otherwise it's just the same classification problem, just with more more labels.\n* Can you make explicit how you think test time intervention could be used in application?\n* Can you run your method NCG without the causal structure to understand how much benefit is attributed to the causal graph itself?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Integrating causal reasoning into deep neural networks is a nice feature of the paper, and not something (to the best of my knowledge) which as been explored before convincingly.\n* The method appears quite general, using WordNet should generalize to most domains.\n* Test time intervention should be useful in a HCI context, in more complex decision making which is higher stakes.", "weaknesses": "* First and foremost I found the paper pretty difficult to grasp exactly what had been done, a lot of it is heavily formalized without any intuitive explanations to facility easy reading, this is really going to limit who can read and build upon the work. Those who work in causal reasoning are mostly used to SCMs and it is pretty hard to understand the core differences this presents to that framework, I would really suggest re-writing the paper drastically. For example, Figure 1 is not even reference until page 4, but it's on page 2, this all makes the paper hard to follow.\n* The reliance on WordNet, while smart, limits the method a lot because not all concepts/relations will be in this dataset.\n* The test time intervention relies on using labels, but what if the human involved doesn't know the labels? The accuracy won't be as good as you are reporting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dFW3793BNA", "reviewer": "Reviewer_CUVg", "summary": "The authors introduce Neural Causal Graph, a framework that builds a DAG over concepts from a neural network to improve performance. The DAG yields small but significant classification accuracy improvements on ImageNet and the CUB Birds dataset. The method and results seem compelling, but the writing of the paper is unclear.", "questions": "- I assume the \"Bird Dataset\" is [CUB-200](https://www.vision.caltech.edu/datasets/cub_200_2011/)? The authors should make this clear in the paper\n- Can the authors explain their test-time intervention training and results more clearly?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper studies an interesting problem of inducing a causal graph over a classification task\n- The paper's results show significant improvements in classification accuracy on 2 datasets\n- The introduced methodology seems to be generally useful, especially for sharing information between many classes", "weaknesses": "- The paper's writing is unclear, especially in the methods section. \n- The paper overclaims about interpretability, e.g. \"This capability not only supports sophisticated post-hoc interpretation but also enables dynamic human-AI interactions\" -- it is unclear that the method yields interpretable concepts or arranges them in a manner that allows humans to understand them. More experiments (ideally with real human users) would help to show this\n  - On a related note, the \"intervention training method\" (Secs 3.2.5 and 4.5) are especially unclear. Why would one expect to be able to intervene on a concept in a CBM baseline and maintain accuracy?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)