🔗 Causal Inference

🎞️ ECCV2024 · 4 paper notes

📌 Same area in other venues: 📷 CVPR2026 (4) · 🔬 ICLR2026 (64) · 💬 ACL2026 (7) · 🧪 ICML2026 (19) · 🤖 AAAI2026 (7) · 🧠 NeurIPS2025 (20)

Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation

This paper proposes a bi-level data pruning strategy, BiLP, which combines static pruning based on empirical loss and dynamic pruning based on individual treatment effect (ITE) to efficiently select the most valuable real samples for dataset distillation. It consistently improves the performance of existing distillation methods in a plug-and-play manner while reducing computational overhead.

Integrating Markov Blanket Discovery into Causal Representation Learning for Domain Generalization

This work proposes the CMBRL framework to discover Markov Blanket (MB) features—the minimal sufficient statistics of the target variable—within the latent space. This replaces the convention of selecting only causal or anti-causal variables in existing methods, constructing an invariant prediction mechanism to achieve cross-domain generalization.

Learning Chain of Counterfactual Thought for Bias-Robust Vision-Language Reasoning

This paper proposes the Counterfactual Bias-Robust Reasoning dataset (CoBRa) and the Chain of Counterfactual Thought (CoCT) method. By constructing edited knowledge graphs and image content, the study evaluates and mitigates knowledge bias in large vision-language models (LVLMs), enabling models to perform step-by-step reasoning rather than relying on biased knowledge. This approach significantly outperforms existing methods on tasks requiring reasoning under knowledge bias.

Understanding Physical Dynamics with Counterfactual World Modeling

This paper proposes Counterfactual World Modeling (CWM), which trains a masked video predictor using a temporally-factored masking policy and designs a "counterfactual prompting" mechanism to extract multiple visual structures (e.g., optical flow, segmentation, keypoints) from a single pre-trained model without fine-tuning, achieving state-of-the-art performance on the Physion benchmark for physical dynamics understanding.