Abstract Treatment effect estimation can assist in effective decision-making in e-commerce,
medicine, and education. One popular application of this estimation lies in the prediction of …

Treatment effect estimation from observational data is a fundamental problem in causal
inference, and its critical challenge is to address the confounding bias arising from the …

Hierarchical multi-granularity classification (HMC) assigns labels at varying levels of detail to
images using a structured hierarchy that categorizes labels from coarse to fine, such as …

Recommending items solely catering to users' historical interests narrows users' horizons.
Recent works have considered steering target users beyond their historical interests by …

Off-Policy Evaluation (OPE) is employed to assess the potential impact of a hypothetical
policy using logged contextual bandit feedback, which is crucial in areas such as …

The goal of uplift modeling is to recommend actions that optimize specific outcomes by
determining which entities should receive treatment. One common approach involves two …

In causal inference involving interacting units (eg, individuals in a contact network), peer
effects quantify how the actions or behaviors of peers (eg, wearing a mask) affect an …