Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem
results in the selection bias issue, degrading the recommendation performance ultimately. A …

Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …

Counterfactual explanations interpret the recommendation mechanism by exploring how
minimal alterations on items or users affect recommendation decisions. Existing …

Off-policy learning has drawn huge attention in recommender systems (RS), which provides
an opportunity for reinforcement learning to abandon the expensive online training …

Estimating individual treatment effects (ITE) from observational data is challenging due to
the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation …

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 …

Reinforcement learning has recently become an active topic in recommender system
research, where the logged data that records interactions between items and users …

Although traditional recommendation methods trained on observational interaction
information have engendered a significant impact in real-world applications, it is challenging …

Heterogeneous treatment effect (HTE) estimation from observational data poses significant
challenges due to treatment selection bias. Existing methods address this bias by …