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

196 papers, page 3 of 4

101. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
102. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
103. NeurIPS 2024Partial Transportability for Domain Generalization
104. NeurIPS 2024Proximal Causal Inference With Text Data
105. NeurIPS 2024Qualitative Mechanism Independence
106. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
107. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
108. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
109. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
110. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
111. NeurIPS 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
112. NeurIPS 2024Structured Learning of Compositional Sequential Interventions
113. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
114. NeurIPS 2024Targeted Sequential Indirect Experiment Design
115. NeurIPS 2024Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
116. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
117. NeurIPS 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
118. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
119. NeurIPS 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
120. NeurIPS 2024Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes
121. NeurIPS 2024When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding
122. NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
123. NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
124. NeurIPS 2023A Measure-Theoretic Axiomatisation of Causality
125. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
126. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
127. NeurIPS 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effect
128. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
129. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
130. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
131. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
132. NeurIPS 2023Attention as Implicit Structural Inference
133. NeurIPS 2023Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
134. NeurIPS 2023Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment
135. NeurIPS 2023C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
136. NeurIPS 2023Causal Component Analysis
137. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
138. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
139. NeurIPS 2023Causal Fairness for Outcome Control
140. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
141. NeurIPS 2023Causal normalizing flows: from theory to practice
142. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
143. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
144. NeurIPS 2023CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
145. NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
146. NeurIPS 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
147. NeurIPS 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
148. NeurIPS 2023Counterfactually Comparing Abstaining Classifiers
149. NeurIPS 2023Deep Non-line-of-sight Imaging from Under-scanning Measurements
150. NeurIPS 2023Detecting hidden confounding in observational data using multiple environments

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.