处理效应论文趋势 2023–2026

主要结论

• 处理效应论文从 2023 年的 75 篇增长到 2026 年的 106 篇，三年间扩大到约 1.4 倍。
• NeurIPS 是发处理效应论文最多的会议，2023–2026 年共 131 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是通用机器学习 · unsupervised and semisupervised learning：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 10 篇。
• 在「因果与公平」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的因果发现是 2.4 倍，处理效应是 1.4 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 2026Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
• ICML 2026Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables
• ICLR 2026ActiveCQ: Active Estimation of Causal Quantities
• CVPR 2026Event Stream Filtering via Probability Flux Estimation
• NeurIPS 2025Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities
• ICML 2025A Meta-learner for Heterogeneous Effects in Difference-in-Differences
• ICLR 2025Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables
• CVPR 2025Any6D: Model-free 6D Pose Estimation of Novel Objects
• NeurIPS 2024Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
• ICML 2024A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
• ICLR 2024A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error
• CVPR 2024LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation
• NeurIPS 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effect
• ICML 2023A Three-regime Model of Network Pruning
• ICLR 2023Causal Estimation for Text Data with (Apparent) Overlap Violations
• CVPR 2023HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions
• NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
• ICML 2026Causal-EPIG: Causally Aligned Active CATE Estimation
• ICLR 2026Foundation Models for Causal Inference via Prior-Data Fitted Networks
• CVPR 2026The Invisible Gorilla Effect in Out-of-distribution Detection

全部处理效应论文

共 131 篇，第 1 / 3 页

1. ICML 2026Bilinear Bandits with Partially Observable Features
2. ICML 2026Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
3. ICML 2026Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables
4. ICML 2026Causal-EPIG: Causally Aligned Active CATE Estimation
5. ICML 2026Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
6. ICML 2026DeepBlip: Estimating Conditional Average Treatment Effects Over Time
7. ICML 2026Doubly Robust Distributionally Robust Offline Contextual Pricing
8. ICML 2026Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
9. ICML 2026Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
10. ICML 2026Even Faster Kernel Matrix Linear Algebra via Density Estimation
11. ICML 2026Feasible Fusion: Constrained Joint Estimation under Structural Non-Overlap
12. ICML 2026Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
13. ICML 2026Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
14. ICML 2026Gradient-Based Causal Tree Ensembles: A Backbone Architecture for Heterogeneous Treatment Effects
15. ICML 2026Improved Distribution Estimation in $\ell_\infty$
16. ICML 2026Learning Treatment Allocations with Risk Control Under Partial Identifiability
17. ICML 2026Learning Treatment Representations for Downstream Instrumental Variable Regression
18. ICML 2026Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
19. ICML 2026Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
20. ICML 2026On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models
21. ICML 2026Rank-Learner: Orthogonal Ranking of Treatment Effects
22. ICML 2026Signal Strength Estimation in Logistic Regression Using Data Splitting
23. ICML 2026Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
24. ICML 2026Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
25. ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
26. ICML 2026Treatment Responder Classification with Abstention
27. ICML 2026Unveiling Prior-Data Fitted Networks on Causal Effect Estimation: Pre-Training or Fine-Tuning?
28. ICML 2025A Meta-learner for Heterogeneous Effects in Difference-in-Differences
29. ICML 2025A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
30. ICML 2025Active Treatment Effect Estimation via Limited Samples
31. ICML 2025Adjustment for Confounding using Pre-Trained Representations
32. ICML 2025AutoCATE: End-to-End, Automated Treatment Effect Estimation
33. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
34. ICML 2025Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation
35. ICML 2025Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects
36. ICML 2025Density Ratio Estimation with Conditional Probability Paths
37. ICML 2025Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation
38. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
39. ICML 2025Doubly Robust Conformalized Survival Analysis with Right-Censored Data
40. ICML 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
41. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
42. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
43. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
44. ICML 2025Learning Representations of Instruments for Partial Identification of Treatment Effects
45. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
46. ICML 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
47. ICML 2025On Fine-Grained Distinct Element Estimation
48. ICML 2025Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
49. ICML 2025PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
50. ICML 2025Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies

常见问题

2023–2026 年四大会议一共有多少篇处理效应论文？

共 385 篇，按年份分别是 2023 年 75 篇、2024 年 106 篇、2025 年 98 篇、2026 年 106 篇。

NeurIPS 2026 有多少篇处理效应论文？

NeurIPS 2026 有 44 篇处理效应论文，上一届是 34 篇。

ICLR 2026 有多少篇处理效应论文？

ICLR 2026 有 18 篇处理效应论文，上一届是 16 篇。

ICML 2026 有多少篇处理效应论文？

ICML 2026 有 27 篇处理效应论文，上一届是 31 篇。

CVPR 2026 有多少篇处理效应论文？

CVPR 2026 有 17 篇处理效应论文，上一届是 17 篇。

哪个会议的处理效应论文最多？

NeurIPS，2023–2026 年共 131 篇。