Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …

There are two seemingly unrelated approaches to weighting in observational studies. One of
them maximizes the fit of a model for treatment assignment to then derive weights—we call …

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

Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …

Background Propensity score adjustment is a popular approach for confounding control in
observational studies. Reliable frameworks are needed to determine relative propensity …

Due to recent theoretical findings and advances in statistical computing, there has been a
rapid development of techniques and applications in the area of missing data analysis …

Covariate adjustment is a commonly used method for total causal effect estimation. In recent
years, graphical criteria have been developed to identify all valid adjustment sets, that is, all …

Since artificial intelligence has seen tremendous recent successes in many areas, it has
sparked great interest in its potential for trustworthy and interpretable risk prediction …

Controlling for confounding bias is crucial in causal inference. Distinct methods are currently
employed to mitigate the effects of confounding bias. Each requires the introduction of a set …

When causal effects are to be estimated from observational data, we have to adjust for
confounding. A central aim of covariate selection for causal inference is therefore to …