Improving the conduct and reporting of newer methodological approaches Causal inference, the multidisciplinary field focused ...

Yılmaz, Övünç; Son, Yoonseock; Shang, Guangzhi; Arslan, Hayri A. Causal inference under selection on observables in operations management research: Matching methods and synthetic controls. Journal of ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...

Political Analysis, Vol. 26, No. 1 (January 2018), pp. 54-71 (18 pages) Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of ...

Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has ...

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 ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

Hypertension is among the leading cardiovascular diseases. Despite extensive research, evidence concerning the relationship between long-term exposure to ambient particulate matter and hypertension ...