  Recently, recommender system (RS) based on causal inference has gained much
attention in the industrial community, as well as the states of the art
performance in many prediction and debiasing tasks. Nevertheless, a unified
causal analysis framework has not been established yet. Many causal-based
prediction and debiasing studies rarely discuss the causal interpretation of
various biases and the rationality of the corresponding causal assumptions. In
this paper, we first provide a formal causal analysis framework to survey and
unify the existing causal-inspired recommendation methods, which can
accommodate different scenarios in RS. Then we propose a new taxonomy and give
formal causal definitions of various biases in RS from the perspective of
violating the assumptions adopted in causal analysis. Finally, we formalize
many debiasing and prediction tasks in RS, and summarize the statistical and
machine learning-based causal estimation methods, expecting to provide new
research opportunities and perspectives to the causal RS community.
