The estimation of average treatment effects based on observational data is extremely
important in practice and has been studied by generations of statisticians under different …

The most common way for treating item non‐response in surveys is to construct one or more
replacement values to fill in for a missing value. This process is known as imputation. We …

Federated or multi-site studies have distinct advantages over single-site studies, including
increased generalizability, the ability to study underrepresented populations, and the …

In recommender systems, a common problem is the presence of various biases in the
collected data, which deteriorates the generalization ability of the recommendation models …

We provide a critical review on calibration methods developed in three different areas:
survey sampling, missing data analysis and causal inference. We highlight the connections …

Item nonresponse in surveys is often treated through some form of imputation. We introduce
multiply robust imputation in finite population sampling. This is closely related to multiple …

While model selection is a well-studied topic in parametric and nonparametric regression or
density estimation, selection of possibly high-dimensional nuisance parameters in …

Quantile estimation has attracted significant research interest in recent years. However,
there has been only a limited literature on quantile estimation in the presence of incomplete …

Inverse probability weighting (IPW) and multiple imputation are two widely adopted
approaches dealing with missing data. The former models the selection probability, and the …

A fundamental assumption used in causal inference with observational data is that treatment
assignment is ignorable given measured confounding variables. This assumption of no …