Causal Inference

Consistency of Neural Causal Partial Identification NeurIPS 2024 Continuous Treatment Effects with Surrogate Outcomes ICML 2024 CoreRec: A Counterfactual Correlation Inference for Next Set Recommendation AAAI 2024 Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training ICML 2024 CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series AAAI 2024 Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables NeurIPS 2024 De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts CVPR 2024 DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation NeurIPS 2024 Detecting and Identifying Selection Structure in Sequential Data ICML 2024 Detecting and Measuring Confounding Using Causal Mechanism Shifts NeurIPS 2024 DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy ACL 2024 Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach AAAI 2024 DiffPO: A causal diffusion model for learning distributions of potential outcomes NeurIPS 2024 DiffuCOMET: Contextual Commonsense Knowledge Diffusion ACL 2024 Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs ACL 2024 Discovering Agents (Abstract Reprint) AAAI 2024 Discovering Heterogeneous Causal Effects in Relational Data AAAI 2024 Dissecting the Failure of Invariant Learning on Graphs NeurIPS 2024 Distilling Causal Effect of Data in Continual Few-shot Relation Learning COLING 2024 Do causal predictors generalize better to new domains? NeurIPS 2024 DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models JMLR 2024 Dual Likelihood for Causal Inference under Structure Uncertainty CLeaR 2024 Effective Causal Discovery under Identifiable Heteroscedastic Noise Model AAAI 2024 Enhancing Sufficient Dimension Reduction via Hellinger Correlation ICML 2024 Estimating Bounds on Causal Effects Considering Unmeasured Common Causes PGM 2024