Title: Automatic Visual Instrumental Variable Learning for Confounding-Resistant Domain Generalization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Domain generalization

visual instrumental variable

causal representation learning
Abstract: Many confounding-resistant domain generalization methods for image classification have been developed based on causal interventions. However, their reliance on strong assumptions limits their effectiveness in handling unobserved confounders. Although recent work introduces instrumental variables (IVs) to overcome this limitation, the reliance on manually predefined instruments, particularly in the context of visual data, may result in severe bias or invalidity when IV conditions are violated. To address these issues, we propose a novel approach to automatically learning Visual Instrumental Variables for confounding-resistant Domain Generalization (VIV-DG). We observe that certain non-causal visual attributes in image data naturally satisfy the basic conditions required for valid IVs. Motivated by this insight, we propose the visual instrumental variable, a novel concept that extends classical IV theory to the visual domain. Furthermore, we develop an automatic visual instrumental variable learner that enforces IV conditions on learned representations, enabling the automatic learning of valid visual instrumental variables from image data. Ultimately, VIV-DG inherits the strengths of classical IVs to mitigate unobserved confounding and avoids the significant bias caused by violations of IV conditions in predefined IVs. Extensive experiments on multiple benchmarks verify that VIV-DG achieves superior generalization ability.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes Visual Instrumental Variable Learning for confounding-resistant domain generalization, which introduces instrumental variables to address the challenge of identifying confounders. The optimization process is decoupled into three stages:\n(i) The first stage utilizes frequency-based augmentation to learn causal factors under the constraints of MMD and classification loss;\n(ii) The second stage aims to learn implicit visual instrumental variables using three designed loss functions;\n(iii) The third stage trains a regressor to learn causal factors responsible for classification.\nExperiments are conducted on PACS, VLCS, Office-Home, and Digits-DG.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(i) The proposed visual instrumental variable learning is theoretically effective and interesting for causal representation learning.\n(ii) The design of the three loss functions adheres to the principles of instrumental variable learning.\n(iii) The experiments outperform some existing methods.\nWeaknesses: There are several issues that need to be addressed:\n(i) Although the theory is interesting, the modeling may not be appropriate. The causal graph (i.e., Figure 4) appears incorrect—why do unobserved confounders not affect the input data? Although the appendix provides an explanation for the partial arrows in the figure, further clarification and examples are needed, as causal theory is inherently abstract.\n(ii) The experimental results are not convincing. The method is not evaluated on the standard DomainBed benchmark, and comparisons with more other causal learning-based methods are required.\n(iii) The optimization process is complex. How does its time complexity compare with other methods?\n(iv) The example involving only color and texture may not be sufficient to identify confounders. More comprehensive examples are needed.\n(v) The paper should clarify what can be visualized in the original figure or visualize the features learned for the instrumental variable and causal features SSS.\n(vi) The ablation studies are simple and lack sufficient investigation into the three designed loss functions.\n\nQUESTIONS:\n1. Why is VIV-DG not included in Table 2?\n2. How are the positive and negative samples generated? How are the confounder indices identified in Sections 3.4.2 and 3.4.3?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a novel confounding-resistant DG method termed VIV-DG. The VIV-DG automatically learns visual instrumental variables to effectively mitigate the effects of both observed and unobserved confounders and improve domain generalization. However, obtaining valid IVs remains a challenge. The paper defines the novel concept of visual instrumental variables and develops an IVs learning framework composed of three alternately optimized subnetworks that automatically learns valid ones.  Extensive experiments verify the effectiveness of VIV-DG on generalization.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper is well-structured, and its concepts are clearly defined.
2. The motivation is clear. Considering the intrinsic differences among non-casual factors and identifying the valid instrumental variables to improve generalization is reasonable and promising.
3. The visual explanation well illustrated the effectiveness of VIV-DG in capturing the core object structures.

Weaknesses:
1. The ablation study is not complete. The ablation study should provide a more in-depth analysis of each component in Visual IV learning and the regression predictor, such as relevance-constrained learning.
2. Missing experimental results on other common backbones, such as Vision Transformer.
3. Missing classification experiments on large-scale datasets (such as ImageNet), as well as experiments on other OOD generalization datasets like (ImageNet-Sketch).
4. Missing theoretical proofs of the VIV-DG, as well as analysis and proofs of the generalization's lower and upper bounds.
5. Missing experiments and analysis of the VIV-DG 's efficiency.

QUESTIONS:
See the Weaknesses.

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

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel framework named VIV-DG, which can learn visual instrumental variables in causal domain generalization. It identifies key visual attributes and instrumental variables, in order to retain useful non-causal information and ensures the retention through three different learning paradigms. Results showcase improved performance on causality-driven domain generalization methods and on other baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The identification of instrumental variables is novel and interesting, as encoding critical information in a way that is not detrimental towards the generalization of the model seems to provide more information.\n* The pipeline descriptions is sound, providing clear explanations on how the framework can identify causal variables and non-causal variables.\n\nWeaknesses:\n* The results on the task seem a bit random. Authors cite that in PACS that the framework surpasses all baselines, when in actuality it is equal or lower in three out of four domains, despite being higher on average. \n* No explanation is given on why the Lite version of the model is performing better on certain domains. There is also no experiment with the normal version of the model on VLCS.\n* The framework is said to increase computational costs with larger backbones, but the performance gain on the reported datasets seems marginal for the amount of training resources.\n\nQUESTIONS:\n* Could the authors explain why the results vary in their experiments with both versions of the model and on how many iterations or different seeds the results were evaluated?\n* Are there any ablations done on why the Lite version of the model performs as good as the normal model on certain domains?\n* How much VRAM does it take to train this framework and how could it scale on larger backbones for domain generalization training? Are the training resources worth the increase in performance or model explanation?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces VIV-DG, a novel method for confounding-resistant domain generalization in image classification. The central contribution is the concept of a \"Visual Instrumental Variable\" (Visual IV), which proposes to automatically learn representations from image data that satisfy the conditions of an instrumental variable (IV). This method refines the causal model by partitioning non-causal factors into confounders and useful IVs. The framework consists of three main components trained via a multi-stage alternating optimization: 1) a causal feature extractor, 2) a visual IV learner that is explicitly optimized to enforce the IV conditions of relevance, independence, and exclusion using mutual information objectives, and 3) a regression predictor to generate debiased causal features. The authors claim this approach effectively mitigates the effects of unobserved confounders and outperforms existing state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**:\n\n1. The core idea of *learning* an instrumental variable representation directly from visual data, rather than relying on predefined, potentially invalid instruments (as in IV-DG), is a significant and novel conceptual contribution to the causal DG literature. The proposed structural causal model in Figure 1c, which explicitly separates non-causal factors into confounders and IVs, provides a more nuanced and promising theoretical framing for the problem than the standard approach of discarding all non-causal information.\n\n2. The translation of the three classical IV conditions into differentiable loss functions using mutual information neural estimation and KL-divergence is technically sound and well-motivated. The paper is also thorough in its experimental evaluation, testing the proposed method across four standard DG benchmarks (Digits-DG, PACS, Office-Home, VLCS) and comparing it against a wide range of baselines.\n\n### **Weaknesses**\n\n1. The \"automatic\" IV learner depends critically on a heuristic masking procedure to separate IV-relevant features (texture/color) from causal features (shape). This mask is generated by thresholding the pixel-wise difference between grayscale images (Appendix D). This is a brittle, handcrafted step at the core of an otherwise \"automatic\" learning pipeline. There is no analysis of its robustness (e.g., to the choice of threshold δ) or verification that it truly isolates the intended features.\n\n2. The primary motivation for this work is to improve upon methods like IV-DG that use predefined instruments. However, the comparison to IV-DG is limited, and the authors fail to include it on the Digits-DG benchmark. A more direct and analytical comparison showing why the learned IV succeeds where the predefined one fails is needed, rather than just reporting aggregate scores.\n\n3. The reported performance gains over the strongest baselines are marginal on several key datasets. For instance, the improvement on PACS is approximately 0.4% over FAGT, and on Office-Home, it is 0.3% over CIRL. While SOTA is achieved, these modest gains, coupled with the model's immense complexity and questionable assumptions, fail to provide a compelling conceptual takeaway. The Grad-CAM visualizations (Fig. 6) are confusing because I do not observe a noticeable difference between baseline and the author's method. It also does not provide definitive proof that the model is learning more \"causal\" representations.\n\nQUESTIONS:\n1. The paper uses proxy losses to enforce the IV conditions but only evaluates success via downstream accuracy. Can you provide a more direct verification that the learned representation Z actually satisfies the IV conditions post-training? For example, please report the estimated values of I(Z;S), I(Z;U), and I(Y;Z|S) on a held-out set and compare them to a baseline without these explicit constraints. A satisfactory answer here would provide direct evidence for the central claim of the paper.\n2. The Grad-CAM visualizations (Fig. 6) are confusing because I do not observe a noticeable difference between baseline and the author's method. It also does not provide definitive proof that the model is learning more \"causal\" representations. Can you provide a toy example showing the effectiveness of the proposed method on learning causal representations?\n\nLIMITATIONS:\nThe authors acknowledge the computational cost of the regressor (Remark 2) and briefly mention generic failure modes (Appendix H).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}