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Recently, recommender system (RS) based on causal inference has gained muchattention in the industrial community, as well as the states of the artperformance in many prediction and debiasing tasks. Nevertheless, a unifiedcausal analysis framework has not been established yet. Many causal-basedprediction and debiasing studies rarely discuss the causal interpretation ofvarious biases and the rationality of the corresponding causal assumptions. Inthis paper, we first provide a formal causal analysis framework to survey andunify the existing causal-inspired recommendation methods, which canaccommodate different scenarios in RS. Then we propose a new taxonomy and giveformal causal definitions of various biases in RS from the perspective ofviolating the assumptions adopted in causal analysis. Finally, we formalizemany debiasing and prediction tasks in RS, and summarize the statistical andmachine learning-based causal estimation methods, expecting to provide newresearch opportunities and perspectives to the causal RS community.

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges