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 …

In the social sciences, randomized experimentation is the optimal research design for
establishing causation. However, for a number of practical reasons, researchers are …

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 …

Social scientists increasingly exploit natural experiments in their research. This article
surveys recent applications in political science, with the goal of illustrating the inferential …

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

We show that propensity score matching (PSM), an enormously popular method of
preprocessing data for causal inference, often accomplishes the opposite of its intended …

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 …

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 …

Political scientists are often interested in estimating causal effects. Identification of causal
estimates with observational data invariably requires strong untestable assumptions. Here …

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