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 ...

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 ...

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...

Despite the contributions of more than 700,000 men to randomized controlled trials (RCTs) of prostate cancer (PC) screening over several decades, it is still unclear whether there is a PC-specific ...

Bayesian networks are probabilistic graphical models that encode conditional dependencies among variables within a directed acyclic graph. In the context of causal inference, these networks provide a ...

Real-world data (RWD) is increasingly used for causal inference in healthcare research, but generating credible, decision-ready insights requires more than access to data. It demands intentional ...

Designs and analytic techniques for studies that contribute to causal determinations are in a continuous state of development and evolution. There have been significant shifts in the way study goals ...

LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.

Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...

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 ...