causal inference
A method in statistics and machine learning used to determine causal relationships between variables rather than mere correlations. In AI, causal inference helps in understanding how changes to one variable may affect others, which is crucial for decision-making and policy evaluation.
- A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning
- An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
- Causal Explanation-Guided Learning for Organ Allocation
- Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
- CausalVTG: Towards Robust Video Temporal Grounding via Causal Inference
- Coupling Generative Modeling and an Autoencoder with the Causal Bridge
- Cyclic Counterfactuals under Shift–Scale Interventions
- Data Fusion for Partial Identification of Causal Effects
- DeCaFlow: A deconfounding causal generative model
- Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
- Do-PFN: In-Context Learning for Causal Effect Estimation
- Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
- Faster Generic Identification in Tree-Shaped Structural Causal Models
- GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
- Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
- Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
- It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
- Learning Counterfactual Outcomes Under Rank Preservation
- Multimodal Causal Reasoning for UAV Object Detection
- Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
- Online Time Series Forecasting with Theoretical Guarantees
- Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
- PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
- Path-specific effects for pulse-oximetry guided decisions in critical care
- Prediction-Powered Causal Inferences
- ProDAG: Projected Variational Inference for Directed Acyclic Graphs
- Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition
- Stochastic Gradients under Nuisances
- Universal Causal Inference in a Topos
- Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization
- When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
- iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning