Domain Generalization -- A Causal Perspective

Machine learning models have gained widespread success, from healthcare to personalized recommendations. One of the preliminary assumptions of these models is the independent and identical distribution. Therefore, the train and test data are sampled from the same observation per this assumption. However, this assumption seldom holds in the real world due to distribution shifts. Since the models rely heavily on this assumption, they exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, none of the existing surveys categorize the causal domain generalization methods based on the problem and causal theories these methods leverage. To this end, we present a comprehensive survey on causal domain generalization models from the aspects of the problem and causal theories. Furthermore, this survey includes in-depth insights into publicly accessible datasets and benchmarks for domain generalization in various domains. Finally, we conclude the survey with insights and discussions on future research directions. Finally, we conclude the survey with insights and discussions on future research directions.

Causal Disentanglement with Network Information for Debiased Recommendations

Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of information about multiple factors, including user's interests, item attributes along with confounding factors such as user conformity, and item popularity. Considering these entangled representations for inferring user preference may lead to biased recommendations (e.g., when the recommender model recommends popular items even if they do not align with the user's interests). Recent research proposes to debias by modeling a recommender system from a causal perspective. The exposure and the ratings are analogous to the treatment and the outcome in the causal inference framework, respectively. The critical challenge in this setting is accounting for the hidden confounders. These confounders are unobserved, making it hard to measure them. On the other hand, since these confounders affect both the exposure and the ratings, it is essential to account for them in generating debiased recommendations. To better approximate hidden confounders, we propose to leverage network information (i.e., user-social and user-item networks), which are shown to influence how users discover and interact with an item. Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders. Experiments on real-world datasets validate the effectiveness of the proposed model for debiasing recommender systems.