As the counterfactual model of causality has increased in popularity, sociologists have
returned to matching as a research methodology. In this article, advances over the past two …

Researchers who generate data often optimize efficiency and robustness by choosing
stratified over simple random sampling designs. Yet, all theories of inference proposed to …

Matching methods for causal inference selectively prune observations from the data in order
to reduce model dependence. They are successful when simultaneously maximizing …

When estimating causal effects using observational data, it is desirable to replicate a
randomized experiment as closely as possible by obtaining treated and control groups with …

Matching methods improve the validity of causal inference by reducing model dependence
and offering intuitive diagnostics. Although they have become a part of the standard tool kit …

Many areas of political science focus on causal questions. Evidence from statistical analyses
is often used to make the case for causal relationships. While statistical analyses can help …

▪ Abstract When experimental designs are infeasible, researchers must resort to the use of
observational data from surveys, censuses, and administrative records. Because …

In recent years, there has been a burst of innovative work on methods for estimating causal
effects using observational data. Much of this work has extended and brought a renewed …

For misguided reasons, social scientists have long been reluctant to use gain scores for
estimating causal effects. This article develops graphical models and graph-based …

Although published works rarely include causal estimates from more than a few model
specifications, authors usually choose the presented estimates from numerous trial runs …