← Reasoning
Knowledge & Reasoning › Reasoning ›

Causal Inference

934 directly classified papers

Papers per year

Papers

Alleviating Dual Biases in Recommendation (Student Abstract) AAAI 2025
Disentangling Biased Representations: A Causal Intervention Framework for Fairer NLP Models ACL 2025
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models ACL 2025
Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs NAACL 2025
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey NAACL 2025
Unzipping the Causality of Zipf’s Law and Other Lexical Trade-offs NAACL 2025
On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference JMLR 2025
CoDeR: Counterfactual Demand Reasoning for Sequential Recommendation AAAI 2025
C2MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis ICCV 2025
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality AAAI 2025
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models AAAI 2025
Estimating Network-Mediated Causal Effects via Principal Components Network Regression JMLR 2025
Learning causal graphs via nonlinear sufficient dimension reduction JMLR 2025
Identifying Causal Mechanism Shifts Under Additive Models with Arbitrary Noise IJCAI 2025
DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path EMNLP 2025
Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding AAAI 2025
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences AAAI 2024
Detecting and Measuring Confounding Using Causal Mechanism Shifts NIPS 2024
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants AAAI 2024
Effective Causal Discovery under Identifiable Heteroscedastic Noise Model AAAI 2024
An Information-Flow Perspective on Algorithmic Fairness AAAI 2024
Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise AAAI 2024
Identifiability Analysis of Linear ODE Systems with Hidden Confounders NIPS 2024
Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model AAAI 2024
Towards Learning and Explaining Indirect Causal Effects in Neural Networks AAAI 2024