  To improve user experience and profits of corporations, modern industrial
recommender systems usually aim to select the items that are most likely to be
interacted with (e.g., clicks and purchases). However, they overlook the fact
that users may purchase the items even without recommendations. To select these
effective items, it is essential to estimate the causal effect of
recommendations. The real effective items are the ones which can contribute to
purchase probability uplift. Nevertheless, it is difficult to obtain the real
causal effect since we can only recommend or not recommend an item to a user at
one time. Furthermore, previous works usually rely on the randomized controlled
trial~(RCT) experiment to evaluate their performance. However, it is usually
not practicable in the recommendation scenario due to its unavailable time
consuming. To tackle these problems, in this paper, we propose a causal
collaborative filtering~(CausCF) method inspired by the widely adopted
collaborative filtering~(CF) technique. It is based on the idea that similar
users not only have a similar taste on items, but also have similar treatment
effect under recommendations. CausCF extends the classical matrix factorization
to the tensor factorization with three dimensions -- user, item, and treatment.
Furthermore, we also employs regression discontinuity design (RDD) to evaluate
the precision of the estimated causal effects from different models. With the
testable assumptions, RDD analysis can provide an unbiased causal conclusion
without RCT experiments. Through dedicated experiments on both the public
datasets and the industrial application, we demonstrate the effectiveness of
our proposed CausCF on the causal effect estimation and ranking performance
improvement.
