反事实解释论文趋势 2023–2026

主要结论

• 反事实解释论文从 2023 年的 21 篇增长到 2026 年的 26 篇，三年间扩大到约 1.2 倍。
• NeurIPS 是发反事实解释论文最多的会议，2023–2026 年共 31 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是反事实公平：同时属于两个主题的论文从 2023 年的 8 篇增至 2026 年的 10 篇。
• 在「可解释性」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的机制解释是 8.1 倍，反事实解释是 1.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Offloading Score: Measuring AI Reliance through Counterfactual Workflows
• ICML 2026Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
• ICLR 2026Training-free Counterfactual Explanation for Temporal Graph Model Inference
• CVPR 2026Back to the Feature Explaining Video Classifiers with Video Counterfactual
• NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
• ICML 2025Exogenous Isomorphism for Counterfactual Identifiability
• ICLR 2025Generating Likely Counterfactuals Using Sum-Product Networks
• CVPR 2025Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition
• NeurIPS 2024AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
• ICML 2024CF-OPT: Counterfactual Explanations for Structured Prediction
• ICLR 2024Counterfactual Density Estimation using Kernel Stein Discrepancies
• CVPR 2024Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning
• NeurIPS 2023[Re] FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
• ICML 2023Counterfactual Analysis in Dynamic Latent State Models
• ICLR 2023Measuring Forgetting of Memorized Training Examples
• CVPR 2023OCTET: Object-Aware Counterfactual Explanations
• NeurIPS 2026Tracing Actual Causes with Counterfactual Witness Maps
• ICML 2026Algorithmic Recourse of In-Context Learning for Tabular Data
• ICLR 2026ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
• CVPR 2026Making the Classification Explanation Faithful to the Confidence Score

全部反事实解释论文

共 90 篇，第 1 / 2 页

1. CVPR 2026Back to the Feature Explaining Video Classifiers with Video Counterfactual
2. CVPR 2026Making the Classification Explanation Faithful to the Confidence Score
3. ICLR 2026ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
4. ICLR 2026Counterfactual Explanations on Robust Perceptual Geodesics
5. ICLR 2026Counterfactual Structural Causal Bandits
6. ICLR 2026Training-free Counterfactual Explanation for Temporal Graph Model Inference
7. ICLR 2026Unifying Formal Explanations: A Complexity-Theoretic Perspective
8. ICML 2026Algorithmic Recourse of In-Context Learning for Tabular Data
9. ICML 2026Counterfactual Bootstrap for Robust Meta-Reinforcement Learning
10. ICML 2026Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
11. ICML 2026Reward-Preserving Counterfactual State Editing for Offline Reinforcement Learning
12. NeurIPS 2026Can the Parts Fool the Test? Counterfactual Pairing Cycles for Relational OOD
13. NeurIPS 2026COSAC: Counterfactual Credit Assignment in Sequential Cooperative Teams
14. NeurIPS 2026Counterfactual Debugging the World Model Transfer Gap
15. NeurIPS 2026Counterfactual Estimation under Composite Treatments via Progressive Distribution Alignment
16. NeurIPS 2026Counterfactual Explanations for Time-Series Classification via Constrained Flow Matching
17. NeurIPS 2026Counterfactual Predictive State Representations: The Intrinsic Dimension of Partial-Information Games
18. NeurIPS 2026Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
19. NeurIPS 2026CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
20. NeurIPS 2026DegradeQuery: Counterfactual Tuple Pretraining for Context-Aware PROTAC Degradation Prediction
21. NeurIPS 2026Neural-DISCO: Source-Conditioned Counterfactual Editing of Neural Population Activity
22. NeurIPS 2026Offloading Score: Measuring AI Reliance through Counterfactual Workflows
23. NeurIPS 2026On-Policy Counterfactual Influence
24. NeurIPS 2026Second-Order Complexity Theory for Risk, Explanation, and Calibration in Machine Learning
25. NeurIPS 2026Supervision Recovery for Time Series Anomaly Detection via Counterfactual Pairing
26. NeurIPS 2026Tracing Actual Causes with Counterfactual Witness Maps
27. CVPR 2025Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
28. CVPR 2025Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition
29. ICLR 2025Counterfactual Concept Bottleneck Models
30. ICLR 2025Generating Likely Counterfactuals Using Sum-Product Networks
31. ICLR 2025Interpreting Language Reward Models via Contrastive Explanations
32. ICLR 2025Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
33. ICLR 2025Reasoning Elicitation in Language Models via Counterfactual Feedback
34. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
35. ICML 2025Algorithmic Recourse for Long-Term Improvement
36. ICML 2025CoDy: Counterfactual Explainers for Dynamic Graphs
37. ICML 2025COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
38. ICML 2025Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation
39. ICML 2025Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making
40. ICML 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
41. ICML 2025Exogenous Isomorphism for Counterfactual Identifiability
42. ICML 2025Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
43. NeurIPS 2025A Hierarchy of Graphical Models for Counterfactual Inferences
44. NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
45. NeurIPS 2025Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
46. NeurIPS 2025Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
47. NeurIPS 2025LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
48. NeurIPS 2025Learning Counterfactual Outcomes Under Rank Preservation
49. NeurIPS 2025Learning Gradient Boosted Decision Trees with Algorithmic Recourse
50. CVPR 2024Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning

常见问题

2023–2026 年四大会议一共有多少篇反事实解释论文？

共 90 篇，按年份分别是 2023 年 21 篇、2024 年 20 篇、2025 年 23 篇、2026 年 26 篇。

NeurIPS 2026 有多少篇反事实解释论文？

NeurIPS 2026 有 15 篇反事实解释论文，上一届是 7 篇。

ICLR 2026 有多少篇反事实解释论文？

ICLR 2026 有 5 篇反事实解释论文，上一届是 5 篇。

ICML 2026 有多少篇反事实解释论文？

ICML 2026 有 4 篇反事实解释论文，上一届是 9 篇。

CVPR 2026 有多少篇反事实解释论文？

CVPR 2026 有 2 篇反事实解释论文，上一届是 2 篇。

哪个会议的反事实解释论文最多？

NeurIPS，2023–2026 年共 31 篇。