  Traditional recommender systems aim to estimate a user's rating to an item
based on observed ratings from the population. As with all observational
studies, hidden confounders, which are factors that affect both item exposures
and user ratings, lead to a systematic bias in the estimation. Consequently, a
new trend in recommender system research is to negate the influence of
confounders from a causal perspective. Observing that confounders in
recommendations are usually shared among items and are therefore multi-cause
confounders, we model the recommendation as a multi-cause multi-outcome (MCMO)
inference problem. Specifically, to remedy confounding bias, we estimate
user-specific latent variables that render the item exposures independent
Bernoulli trials. The generative distribution is parameterized by a DNN with
factorized logistic likelihood and the intractable posteriors are estimated by
variational inference. Controlling these factors as substitute confounders,
under mild assumptions, can eliminate the bias incurred by multi-cause
confounders. Furthermore, we show that MCMO modeling may lead to high variance
due to scarce observations associated with the high-dimensional causal space.
Fortunately, we theoretically demonstrate that introducing user features as
pre-treatment variables can substantially improve sample efficiency and
alleviate overfitting. Empirical studies on simulated and real-world datasets
show that the proposed deep causal recommender shows more robustness to
unobserved confounders than state-of-the-art causal recommenders. Codes and
datasets are released at https://github.com/yaochenzhu/deep-deconf.
