  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 counterfactual modeling can help us to think outside of the observational
data for user modeling and personalization so as to tackle such issues. In this
paper, we propose Causal Collaborative Filtering (CCF) -- a general framework
for modeling causality in collaborative filtering and recommendation. We
provide a unified causal view of CF and mathematically show that many of the
traditional CF algorithms are actually special cases of CCF under simplified
causal graphs. We then propose a conditional intervention approach for
$do$-operations so that we can estimate the user-item causal preference based
on the observational data. Finally, we further propose a general counterfactual
constrained learning framework for estimating the user-item preferences.
Experiments are conducted on two types of real-world datasets -- traditional
and randomized trial data -- and results show that our framework can improve
the recommendation performance and reduce the Simpson's paradox problem of many
CF algorithms.
