🔗 Causal Inference

🎞️ ECCV2026 · 3 paper notes

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

CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics

The paper detaches the CausalVAE causal layer into a latent-space plug-in that sits between any encoder and any action-conditioned transition backbone and organizes latents through a learnable DAG adjacency matrix; without touching the base prediction architecture it sharply improves intervention-aware counterfactual retrieval (CF-H@1 on Physics 3-body rises from 11.0 to 41.0 for GNN_NLL) while largely preserving factual prediction.

SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

SPARC turns path-specific counterfactual fairness for high-dimensional inputs into a label-conditioned sensitive-attribute independence objective, using mutual-information pretraining to retain diagnostic information and conditional adversarial masks to suppress unfair pathways, improving fairness on three medical datasets while retaining a predictive-performance cost relative to unconstrained models.

FD\(^2\): A Dedicated Framework for Fine-Grained Dataset Distillation

Addressing the issue that existing decoupled dataset distillation methods ignore discriminative local regions on fine-grained datasets, FD\(^2\) introduces Counterfactual Attention Learning (CAL) to extract discriminative attention maps and class prototypes. Integrating fine-grained feature constraints and intra-class sample similarity constraints during the distillation phase, it serves as a plug-and-play module that significantly improves the distillation quality of SRe2L++ and FADRM+ on fine-grained datasets such as CUB-200-2011, FGVC-Aircraft, and Stanford Cars, achieving a maximum improvement of +15.1% at IPC=1.