The goal of recommendation is to show users items that they will like. Though
usually framed as a prediction, the spirit of recommendation is to answer an
interventional question---for each user and movie, what would the rating be if
we "forced" the user to watch the movie? To this end, we develop a causal
approach to recommendation, one where watching a movie is a "treatment" and a
user's rating is an "outcome." The problem is there may be unobserved
confounders, variables that affect both which movies the users watch and how
they rate them; unobserved confounders impede causal predictions with
observational data. To solve this problem, we develop the deconfounded
recommender, a way to use classical recommendation models for causal
recommendation. Following Wang & Blei [23], the deconfounded recommender
involves two probabilistic models. The first models which movies the users
watch; it provides a substitute for the unobserved confounders. The second one
models how each user rates each movie; it employs the substitute to help
account for confounders. This two-stage approach removes bias due to
confounding. It improves recommendation and enjoys stable performance against
interventions on test sets.

推荐系统通过因果分析的方法解决了推荐过程中潜在变量的干扰，引入去混淆因素的推荐模型成功地提高了推荐准确性。