Causal inference
Introduced 2000694 papers
Description
Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.
Papers Using This Method
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction2025-06-24Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies2025-06-24Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition2025-06-23T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent2025-06-23Linear-Time Primitives for Algorithm Development in Graphical Causal Inference2025-06-18Estimation of Treatment Effects in Extreme and Unobserved Data2025-06-16Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts2025-06-12Foundation Models for Causal Inference via Prior-Data Fitted Networks2025-06-12From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations2025-06-12STOAT: Spatial-Temporal Probabilistic Causal Inference Network2025-06-11Correlation vs causation in Alzheimer's disease: an interpretability-driven study2025-06-11Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation2025-06-10CausalPFN: Amortized Causal Effect Estimation via In-Context Learning2025-06-09Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models2025-06-08Quantile-Optimal Policy Learning under Unmeasured Confounding2025-06-08Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants2025-06-05N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion2025-06-04What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness2025-06-04AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving2025-06-04Machine Mirages: Defining the Undefined2025-06-03