  Recommendation systems aim to predict users' feedback on items not exposed to
them.
  Confounding bias arises due to the presence of unmeasured variables (e.g.,
the socio-economic status of a user) that can affect both a user's exposure and
feedback. Existing methods either (1) make untenable assumptions about these
unmeasured variables or (2) directly infer latent confounders from users'
exposure. However, they cannot guarantee the identification of counterfactual
feedback, which can lead to biased predictions. In this work, we propose a
novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of
proxy variables (e.g., observed user features) to resolve the aforementioned
non-identification issue. The proposed iDCF is a general deconfounded
recommendation framework that applies proximal causal inference to infer the
unmeasured confounders and identify the counterfactual feedback with
theoretical guarantees. Extensive experiments on various real-world and
synthetic datasets verify the proposed method's effectiveness and robustness.
