经济因果论文趋势 2023–2026

主要结论

• 经济因果论文从 2023 年的 130 篇降到 2026 年的 27 篇，热度在回落。
• NeurIPS 是发经济因果论文最多的会议，2023–2026 年共 117 篇，占四个会议总量的 64%。
• 交叉方向里涨得最快的是强化学习：同时属于两个主题的论文从 2023 年的 12 篇增至 2026 年的 10 篇。
• 在「金融与经济」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的宏观预测是 8.0 倍，经济因果是 0.2 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 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
• ICML 2026Independent Component Discovery in Temporal Count Data
• ICLR 2026Ice Cream Doesn’t Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
• CVPR 2026COPO Causal-Oriented Policy Optimization for Hallucinations of MLLMs
• NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
• ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
• ICLR 2025Incremental Causal Effect for Time to Treatment Initialization
• NeurIPS 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
• ICML 2024Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness
• ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
• NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
• ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
• ICLR 2023Causal Imitation Learning via Inverse Reinforcement Learning
• CVPR 2023ECON: Explicit Clothed Humans Optimized via Normal Integration
• NeurIPS 2026Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk
• ICML 2026Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
• ICLR 2026Foundation Models for Causal Inference via Prior-Data Fitted Networks
• NeurIPS 2025Pessimistic Data Integration for Policy Evaluation
• ICML 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
• ICLR 2025Efficient Causal Decision Making with One-sided Feedback

全部经济因果论文

共 27 篇，第 1 / 1 页

1. CVPR 2026COPO Causal-Oriented Policy Optimization for Hallucinations of MLLMs
2. ICLR 2026A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
3. ICLR 2026Adjusting Prediction Model Through Wasserstein Geodesic for Causal Inference
4. ICLR 2026Foundation Models for Causal Inference via Prior-Data Fitted Networks
5. ICLR 2026Ice Cream Doesn’t Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
6. ICLR 2026Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
7. ICML 2026Active Policy Optimization for Individualized Dosing via Gradient Variance Minimization
8. ICML 2026COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
9. ICML 2026Ekka: Automated Diagnosis of Silent Errors in LLM Inference
10. ICML 2026Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
11. ICML 2026Fast-SAM3D: 3Dfy Anything in Images but Faster
12. ICML 2026Gradient-Based Causal Tree Ensembles: A Backbone Architecture for Heterogeneous Treatment Effects
13. ICML 2026HodgeFlow Policy Search by Topologically Dissecting Temporal-Difference Signals in Non-Markovian Environments
14. ICML 2026Independent Component Discovery in Temporal Count Data
15. ICML 2026Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
16. ICML 2026Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random
17. ICML 2026REG: In-Sample RL via Regularizing the Evaluation Gap
18. ICML 2026ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
19. ICML 2026Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
20. ICML 2026SpreadsheetArena: Decomposing Preference in LLM Generation of Spreadsheet Workbooks
21. ICML 2026Target-Driven Policy Optimization for Sequential Counterfactual Outcome Control
22. ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
23. ICML 2026Uncovering Bias Mechanisms in Observational Studies
24. NeurIPS 2026Causal Representation Learning for Generalisable Recommendation
25. NeurIPS 2026Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk
26. NeurIPS 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
27. NeurIPS 2026Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs

常见问题

2023–2026 年四大会议一共有多少篇经济因果论文？

共 183 篇，按年份分别是 2023 年 130 篇、2024 年 10 篇、2025 年 16 篇、2026 年 27 篇。

NeurIPS 2026 有多少篇经济因果论文？

NeurIPS 2026 有 4 篇经济因果论文，上一届是 6 篇。

ICLR 2026 有多少篇经济因果论文？

ICLR 2026 有 5 篇经济因果论文，上一届是 4 篇。

ICML 2026 有多少篇经济因果论文？

ICML 2026 有 17 篇经济因果论文，上一届是 6 篇。

CVPR 2026 有多少篇经济因果论文？

CVPR 2026 有 1 篇经济因果论文。

哪个会议的经济因果论文最多？

NeurIPS，2023–2026 年共 117 篇。