Title: Causally Reliable Concept Bottleneck Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Explainable AI

Concept-based models

Causal reliability

Deep learning
Abstract: Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the true causal mechanisms underlying the target phenomena represented in the data. This hampers their ability to support causal reasoning tasks, limits out-of-distribution generalization, and hinders the implementation of fairness constraints. To overcome these issues, we propose Causally reliable Concept Bottleneck Models (C$^2$BMs), a class of concept-based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal mechanisms. We also introduce a pipeline to automatically learn this structure from observational data and unstructured background knowledge (e.g., scientific literature). Experimental evidence suggests that C$^2$BMs are more interpretable, causally reliable, and improve responsiveness to interventions w.r.t. standard opaque and concept-based models, while maintaining their accuracy.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work attempts to bridge the gap between CBMs and Causal reliability with a new framework C2BM. It is based on performing LLM guided causal discovery on an identified concept space and combining this with structured causal modelling. The authors investigate the overall predictive performance as well as the. ability to perform concept interventions and debiasing.\n\nSTRENGTHS AND WEAKNESSES:\nI generally like the idea very much of combining concept XAI with causal reliability. Also the paper is very well written and it is easy to follow. Generally, I have very few weaknesses, just a few clarification issues which it would be good if the authors could clear up my misunderstandings. I am aware that the predictive performance experimental results don't look smashing, but I don't think this should be the focus of this work, i.e. the approach is interesting enough to overlook this.\n\nMainly, I am missing where the label prediction occurs in the method description, but also within figure 1. Maybe I am not an expert enough on SCMs, but how are the classifications of the evaluations done based on the setup, i.e. how is the most likely variable selected from the causal graph bottleneck. Standard CBMs utilize a final predictor, but I am missing the information on how this is handled here.\n\nIn this context I am still a little confused about what the goal is of C2BM and how this differs from simply doing causal discovery. It seems an image is first processed into concepts and then a causal graph is discovered based on this. Could the authors elaborate on this?\n\nIn this context I am also a little puzzled by the broader impact statement \"C2BM has the potential to significantly narrow the hypothesis space in complex scientific domains where even a panel of human experts might struggle to identify or exclude plausible hypotheses worth testing. \" again isn't this \"simply\" causal discovery? I.e. why do we need C2BM for this use case?\n\nThe debiasing experiment is a little unclear to me. How are the interventions being done here? I would assume if a vanilla CBM has color and shape concepts it should be very easy to debias the model, by reducing the reliance on the spurious concept. Could the authors clarify how exactly the results of figure 5 were created, particularly for the baseline models?\n\nI'm a little surprised the effect of C2BMs in Fig. 4 is quite low (often only a few decimal point advantages). I would expect particularly here such models to shine. Could the authors discuss a little bit on this. Do these datasets simply don't require such interventions?\n\nTab. 1 is very squeezed. Do you think it might be an idea to move two columns to the appendix to improve the readability within the main text?\n\nQUESTIONS:\nI would consider citing [1] in terms of concept interventions:\n\n[1] Steinmann, David, Wolfgang Stammer, Felix Friedrich, and Kristian Kersting. \"Learning to intervene on concept bottlenecks.\" arXiv preprint arXiv:2308.13453 (2023).\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

comments: SUMMARY:
The paper introduces Causally-reliable Concept Bottleneck Models (C2BMs), an extension of CBMs that force predictions to flow through an explicit causal graph model. Specifically, the authors propose a neural architecture that couples a concept encoder with a meta-model that adaptively parameterises linear structural equations. To build the causal graph, the authors propose an automated pipeline that discover concepts and edges using a retrieval-augmented LLM. They conduct experiments on multiple datasets showing comparable performance to other CBM architectures but find that C2BMs decrease reliance on spurious correlations and enable interventions to improve fairness.

STRENGTHS AND WEAKNESSES:
**Strenghts:**
- The integration of known causal relationships into CBMs is well motivated
- The proposed architecture combining a learnable encoder with adaptive structural equations is elegant
- The evaluations demonstrates comparable performance to other CBMs while providing better steering ability
- The causal graph construction pipeline using LLMs can reduce reliance on human experts

**Weaknesses:** 
- The causal model injects the graphs's inductive biases into the learner. Thus, if the graph is misspecified, these biases can hurt task accuracy relative to graph-agnostic models. 
- The paper only contrasts the learned graphs against a ground truth graph but does not report performance when the model is trained with random or adversarially corrupted graphs. Thus, it's unclear how sensitive C2BM is to the quality of the causal graph.

QUESTIONS:
- Have you quantified the extend to which the quality of the causal graph is a determining factor for model performance?

LIMITATIONS:
See above
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work deals with the task of building interpretable predictive models for raw data like image and text. While existing methods like Concept Bottleneck Models (CBMs) are transparent in their decision-making, their explanations are not causal, that is, they cannot be used to recommend interventions. This work extends CBMs by making them causal, calling the result Causally reliable Concept Bottleneck Models (C2BMs). This model consists of a set of concepts, a structural causal model (SCM) over the concepts, a neural encoder mapping raw data to a set of latent representations, and a neural encoder mapping each latent representation to a set of weights, used to define structural equations of the SCM.\nThe authors do so using concept bottleneck, that is, using a neural network to map the raw data to a set of interpretable concepts, which are used to make the final decision. The authors extend CBMs by combining them with causal models, learning neural encoders to map raw data to structural causal models of concepts, allowing reliable causal inference.\nThe proposed method consists of three stages. The first stage is concept discovery; they use label free concept discovery and then filter the concepts based on brevity, distinctness and presence in their training data. A pretrained vison-language model like CLIP is used to annotate each data point with these concepts. The second stage is causal discovery; they address this by first constructing a CPDAG using greedy equivalence search and then orienting its edges by querying an LLM. To improve reliability, retrieval augmented generation (RAG) and majority voting across 10 repetitions is used. Finally, the third stage involves learning the structural equations; they approximate each such equation for each variable as a weighted sum of its parents’ values, each parent weighed by the neural encoder’s output. Each structural equation is learned separately, exploiting the Markov condition due to the SCM’s causal graph.\nThe authors evaluate this approach on 5 BN-based domains, 1 benchmark domain color MNIST (cMNIST) domain, and 3 real-world domains: CelebA, facial recognition, annotated with binary facial attributes; CUB_c, a subset of original CUB birds data set, having a subset of concepts and some new ones to introduce deeper causal relationships; and Siim-Pneumothorax, consisting of chest radiograms, annotated with a single label indicating presence of Pneumothorax, augmented with annotations extracted by label-free methods. The empirical evaluation shows that (1) the causal discovery method accurately recovers the underlying causal relationships and C2BM performs on par or better than baseline models, including an opaque NN, (2) C2BM achieves better performance with fewer interventions, (3) causal structure allows C2BM to mitigate the effect of spurious correlations captured by neural encoder, (4) causal structure allows C2BM to eliminate unfair bias from its decision-making.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Highly relevant problem for high stakes domains like healthcare that require interpretability and causal reliability\n2. The proposed pipeline is general enough to use more specialized LLM inference frameworks e.g., Loula et al., (2025).\n\nWeaknesses\n1. Since LLMs are stochastic and sensitive to phrasing of the prompt, the design of the prompt might affect performance.\n2. Since LLMs answer causal questions by approximately retrieving causal facts from their training data, causal graph construction would be harder in understudied domains like rare diseases.\n\nReferences\nLoula, João, et al. \"Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo.\" ICLR, 2025.\n\nQUESTIONS:\nQuestions\n1. What is the effect of data set size and causal graph complexity on the causal discovery method?\n2. While the RAG context has no impact in the Alarm domain, it improves performance on the Sachs domain. Were there any cases where using RAG degraded performance?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Causally reliable Concept Bottleneck Models (C2BMs), which explicitly incorporate causal structure into the concept bottleneck architecture. The authors present a novel pipeline to automatically learn the causal concept structure from observational data and unstructured knowledge sources, aiming to enhance interpretability, causal reliability, and responsiveness to interventions.\n\nSTRENGTHS AND WEAKNESSES:\nThe research tackles an important challenge at the intersection of explainability and causality in deep learning and offers promising directions. However, further clarification on the automatic structure learning process and more extensive evaluation would strengthen the manuscript.\n\nQUESTIONS:\n1. The notion of a \"bottleneck\" presented in the paper is intriguing. However, the manuscript lacks details on how the bottleneck is quantitatively evaluated and how this mechanism influences the model’s operations. I recommend the authors provide a more thorough explanation and possibly include experimental evidence demonstrating its effect.\n\n2. Section 4 cites and utilizes many existing methods, which may obscure the unique contributions of the current study. To improve clarity, the authors should explicitly state the advantages of their approach and clearly differentiate it from prior works.\n\n3. The proposed model does not appear to achieve significantly better task accuracy compared to baseline methods. The authors should discuss possible reasons for this and highlight the practical or theoretical benefits that justify their approach despite the modest performance gains.\n\n4. The training procedure and other experimental details are insufficiently described. Please provide a comprehensive explanation of how the model is trained. Additionally, we suggest including ablation studies from various perspectives discussed in Section 4, such as concept discovery, labeling, and other relevant components, to better understand the contribution of each part.\n5. As the authors mentioned, a robust prior knowledge base is very important to model training and learning. This part is interesting, but not including many details. Suggest the authors explain how to built such a knowledge base. and answer how to evaluate the effectiveness of the built knowledge base, cause that can impact the training performance. Is the pipeline for constructing the knowledge base fully automated? How is the transferability of the knowledge ensured?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}