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

28 papers, page 1 of 1

1. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
2. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
3. NeurIPS 2026CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
4. NeurIPS 2026Causal Abstractions, Categorically Unified
5. NeurIPS 2026Causal Effect Identification with a Single Agnostic Proxy
6. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
7. NeurIPS 2026Causal learning with the invariance principle
8. NeurIPS 2026Causal Survival Forests with Negative Controls
9. NeurIPS 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
10. NeurIPS 2026Confounding-Aware Client Selection in Federated Learning via Causal Mediation Analysis
11. NeurIPS 2026Decision Path Tracing for Causal Analysis in Transformers
12. NeurIPS 2026Decomposing Effects in Neural Causal Models
13. NeurIPS 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
14. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
15. NeurIPS 2026Generalized Bayes for Causal Inference
16. NeurIPS 2026Improving Causal Explanations
17. NeurIPS 2026Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning
18. NeurIPS 2026Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
19. NeurIPS 2026Neural Causal Models under Markov Equivalence
20. NeurIPS 2026Online Causal Configuration for Networked Systems via Doubly Robust Steady-State Learning
21. NeurIPS 2026Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
22. NeurIPS 2026Statistical Inference in Causal Partial Identification under Smooth Densities
23. NeurIPS 2026Structural Causal Bottleneck Models
24. NeurIPS 2026Towards a Universal Causal Reasoner
25. NeurIPS 2026Two-Clustering Regime of Token Dynamics in Causal Attention
26. NeurIPS 2026Who caused $Y$? Local identifiability for learning causal parents
27. NeurIPS 2026Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
28. NeurIPS 2026xWhy: Causal Learning from Explanations

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.