Many of the traditional recommendation algorithms are designed based on the fundamental
idea of mining or learning correlative patterns from data to estimate the user-item correlative
preference. However, pure correlative learning may lead to Simpson's paradox in
predictions, and thus results in sacrificed recommendation performance. Simpson's paradox
is a well-known statistical phenomenon, which causes confusions in statistical conclusions
and ignoring the paradox may result in inaccurate decisions. Fortunately, causal and …

Recommender systems may be confounded by various types of confounding factors (also
called confounders) that may lead to inaccurate recommendations and sacrificed
recommendation performance. Current approaches to solving the problem usually design
each specific model for each specific confounder. However, real-world systems may include
a huge number of confounders and thus designing each specific model for each specific
confounder could be unrealistic. More importantly, except for those “explicit confounders” …

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as
a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback
loops, defined as the cyclic process of recommending items, incorporating user feedback in
model updates, and repeating the procedure. As a result, it is important to incorporate loops
into the causal graphs to accurately model the dynamic and iterative data generation
process for recommender systems. However, feedback loops are not always beneficial since …