F-DACE: Fuzzy Disagreement-Aware Causal Evidence Fusion for Abstention-Safe Conversational Retail Decision Support

arXiv:2609.18238 · cs.LG · Submitted 2026-09-16 · Read on arXiv

cs.LG

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Pages: 21,Figures: 6,Tables: 10

Code: https://github.com/py-why/EconML

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Abstention — 27× in this paper · explained in 1 episode
• Fidelity — 12× in this paper · explained in 2 episodes
• Verifier — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Causal Inference — 4× in this paper · explained in 4 episodes
• Regret — 4× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Arbiter — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Residualization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

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