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

84 papers, page 2 of 2

51. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
52. NeurIPS 2024Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards
53. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
54. NeurIPS 2024Marginal Causal Flows for Validation and Inference
55. NeurIPS 2024Markov Equivalence and Consistency in Differentiable Structure Learning
56. NeurIPS 2024Marrying Causal Representation Learning with Dynamical Systems for Science
57. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
58. NeurIPS 2024Natural Counterfactuals With Necessary Backtracking
59. NeurIPS 2024Off-policy estimation with adaptively collected data: the power of online learning
60. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
61. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
62. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
63. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
64. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
65. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
66. NeurIPS 2024Partial Transportability for Domain Generalization
67. NeurIPS 2024Proximal Causal Inference With Text Data
68. NeurIPS 2024Qualitative Mechanism Independence
69. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
70. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
71. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
72. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
73. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
74. NeurIPS 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
75. NeurIPS 2024Structured Learning of Compositional Sequential Interventions
76. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
77. NeurIPS 2024Targeted Sequential Indirect Experiment Design
78. NeurIPS 2024Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
79. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
80. NeurIPS 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
81. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
82. NeurIPS 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
83. NeurIPS 2024Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes
84. NeurIPS 2024When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding

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.