Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data ...

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...

Recently, a research team from Dankook University in South Korea proposed a new method that utilizes principles of quantum mechanics to solve causal inference problems. This breakthrough provides a ...

Statistics is a branch of math that involves the collection, description, analysis, and inference of conclusions from ...

The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...

This is a preview. Log in through your library . Abstract Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal ...

This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...