  Causality-based recommendation systems focus on the causal effects of
user-item interactions resulting from item exposure (i.e., which items are
recommended or exposed to the user), as opposed to conventional
correlation-based recommendation. They are gaining popularity due to their
multi-sided benefits to users, sellers and platforms alike. However, existing
causality-based recommendation methods require additional input in the form of
exposure data and/or propensity scores (i.e., the probability of exposure) for
training. Such data, crucial for modeling causality in recommendation, are
often not available in real-world situations due to technical or privacy
constraints. In this paper, we bridge the gap by proposing a new framework,
called Propensity Estimation for Causality-based Recommendation (PropCare). It
can estimate the propensity and exposure from a more practical setup, where
only interaction data are available without any ground truth on exposure or
propensity in training and inference. We demonstrate that, by relating the
pairwise characteristics between propensity and item popularity, PropCare
enables competitive causality-based recommendation given only the conventional
interaction data. We further present a theoretical analysis on the bias of the
causal effect under our model estimation. Finally, we empirically evaluate
PropCare through both quantitative and qualitative experiments.
