CAFE: Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning

arXiv:2602.16435 · cs.AI, cs.LG, cs.MA · Submitted 2026-02-18 · Read on arXiv

cs.AI, cs.LG, cs.MA

Submitted: 2026-02-18

Updated: 2026-10-25

Terminology

• Feature Engineering — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Causal Discovery — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 11 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems