Qing Li

Causal Recommendation under Confounder Observability: A Short Survey

Causal inference has emerged as a promising paradigm for recommendation systems, moving beyond correlation-based learning to uncover the causal mechanisms behind user–item interactions. Conventional models assume observed interactions directly reflect users’ true preferences, but this assumption often fails in practice...