🔗 Causal Inference

📷 CVPR2026 · 4 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (64) · 💬 ACL2026 (7) · 🧪 ICML2026 (19) · 🤖 AAAI2026 (7) · 🧠 NeurIPS2025 (19) · 📹 ICCV2025 (2)

A Polynomial Chaos Framework for Causal Discovery in Nonlinear Uncertain Systems

This paper embeds noise terms into structural equations using Polynomial Chaos Expansion (PCE) to develop PCE-LiNGAM. It proves that causal Directed Acyclic Graphs (DAGs) are uniquely identifiable under mild sparsity conditions. Using a polynomial-time algorithm involving "PCE signature contamination testing + recursive sink finding," the method improves average F1 scores from 0.50 to 0.756 on extreme non-Gaussian industrial data while providing uncertainty quantification based on Sobol indices.

CGU-Bayes: Causal Graph Uncertainty-Guided Bayesian Inference for Domain Generalization

Addressing the issue that "causal graphs are inaccurately estimated under data scarcity or noise when using Structural Causal Models (SCM) for domain generalization," this paper moves away from point-estimating a single causal graph. Instead, it performs Bayesian inference on the causal graph posterior, selects a set of Causal Markov Blanket (CMB) features from each sampled graph to train predictors, and performs a weighted ensemble using the "alignment uncertainty" between each graph and the test samples. This approach achieves SOTA performance on datasets with strong distribution shifts, such as BLT and CMNIST.

MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations

MaskDiME is proposed, a training-free diffusion framework that transforms global classifier guidance into decision-driven local editing via an adaptive dual-masking mechanism. This achieves precise and efficient visual counterfactual explanations, with inference speeds over 30 times faster than DiME and GPU memory consumption only one-tenth that of ACE/RCSB.

Retrieving Counterfactuals Improves Visual In-Context Learning

The CIRCLES framework is proposed to retrieve counterfactual examples through attribute-guided composed image retrieval, constructing a dual-channel in-context demonstration of "causality + correlation" to significantly enhance the fine-grained visual reasoning capabilities of VLMs.