Papers
1–3 of 3Research Paper·Jan 15, 2026
Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation
Causal discovery aims to recover ``what causes what'', but classical constraint-based methods (e.g., PC, FCI) suffer from error propagation, and recent LLM-based causal oracles often behave as opaque,...
5.0 viability
Research Paper·Feb 1, 2026
Causal Preference Elicitation
We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graph...
5.0 viability
Research Paper·Mar 5, 2026
Learning Causal Structure of Time Series using Best Order Score Search
Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many disciplines poses several challenges due to temporal depe...
3.0 viability