observational data
Data obtained from observing subjects in their natural settings without interference from the researcher. In AI, such data is often used for training models in settings like reinforcement learning and causal inference.
- Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data
- Causal LLM Routing: End-to-End Regret Minimization from Observational Data
- CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
- Causally Reliable Concept Bottleneck Models
- Counterfactual Identifiability via Dynamic Optimal Transport
- Data Fusion for Partial Identification of Causal Effects
- Differentiable Constraint-Based Causal Discovery
- Do-PFN: In-Context Learning for Causal Effect Estimation
- DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments
- Estimation of Treatment Effects in Extreme and Unobserved Data
- From Black-box to Causal-box: Towards Building More Interpretable Models
- GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
- HoloScene: Simulation‑Ready Interactive 3D Worlds from a Single Video
- Learning Stochastic Multiscale Models
- Less Greedy Equivalence Search
- OceanBench: A Benchmark for Data-Driven Global Ocean Forecasting systems
- Thought Communication in Multiagent Collaboration
- Understanding Generalization in Physics Informed Models through Affine Variety Dimensions