Deep learning · causal inference papers, 2023–2026

Key findings

• Deep learning · causal inference fell from 108 papers in 2023 to 66 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · causal inference work: 196 papers from 2023 to 2026, 62% of the four-venue total.
• The fastest-growing overlap is with Causal Recommendation: papers tagged with both rose from 27 in 2023 to 55 in 2026.
• Among the 11 topics under Deep Learning, it ranks #10 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.6x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
• ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
• ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
• ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
• ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
• ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
• CVPR 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
• NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
• ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
• ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026Caracal: Causal Architecture via Spectral Mixing
• ICLR 2026Counterfactual Structural Causal Bandits
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

All Deep learning · causal inference papers

86 papers, page 1 of 2

1. ICML 2026ARC-Decode: Accelerated Decoding with Risk-Bounded Acceptance
2. ICML 2026Beyond Reactivity: Proactive Adaptive Conformal Inference for Online LLM Factuality
3. ICML 2026Caracal: Causal Architecture via Spectral Mixing
4. ICML 2026Causal Attention with Lookahead Keys
5. ICML 2026Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables
6. ICML 2026Causal Feature Learning via Generalized Rayleigh Quotients
7. ICML 2026Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
8. ICML 2026Causal Preference Elicitation
9. ICML 2026Cerebellar-Inspired Residual Control for Fault Recovery: From Inference-Time Adaptation to Structural Consolidation
10. ICML 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
11. ICML 2026Distributionally Robust Causal Abstractions
12. ICML 2026Evaluating Bivariate Causal Statements Based on Mutual Compatibility
13. ICML 2026Federated Causal Inference on Multi-Site Observational Data via Propensity Score Aggregation
14. ICML 2026Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
15. ICML 2026Identifying Partially Observed Causal Models from Heterogeneous/Nonstationary Data
16. ICML 2026Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective
17. ICML 2026Safe Reinforcement Learning with Preference-based Constraint Inference
18. ICML 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
19. ICML 2026Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
20. ICML 2026SparseInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
21. ICML 2026Stem: Rethinking Causal Information Flow in Sparse Attention
22. ICML 2026Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
23. ICML 2026Target-Driven Policy Optimization for Sequential Counterfactual Outcome Control
24. ICML 2026TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
25. ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
26. ICML 2026Uncovering Bias Mechanisms in Observational Studies
27. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
28. ICML 2025Causal Abstraction Inference under Lossy Representations
29. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
30. ICML 2025Causal Attribution Analysis for Continuous Outcomes
31. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
32. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
33. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
34. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
35. ICML 2025Generalized Random Forests Using Fixed-Point Trees
36. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
37. ICML 2025Isolated Causal Effects of Natural Language
38. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
39. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
40. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
41. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
42. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
43. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
44. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
45. ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
46. ICML 2024Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
47. ICML 2024Causal Effect Identification in LiNGAM Models with Latent Confounders
48. ICML 2024Causal Inference from Competing Treatments
49. ICML 2024Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
50. ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis

FAQ

How many Deep learning · causal inference papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

316 in total: 108 in 2023, 108 in 2024, 34 in 2025, 66 in 2026.

How many Deep learning · causal inference papers are at NeurIPS 2026?

NeurIPS 2026 has 28 Deep learning · causal inference papers, versus 9 the year before.

How many Deep learning · causal inference papers are at ICLR 2026?

ICLR 2026 has 8 Deep learning · causal inference papers, versus 6 the year before.

How many Deep learning · causal inference papers are at ICML 2026?

ICML 2026 has 26 Deep learning · causal inference papers, versus 18 the year before.

How many Deep learning · causal inference papers are at CVPR 2026?

CVPR 2026 has 4 Deep learning · causal inference papers, versus 1 the year before.

Which conference publishes the most Deep learning · causal inference papers?

NeurIPS, with 196 papers from 2023 to 2026.