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 ...
Gene regulatory networks (GRNs) depict the regulatory mechanisms of genes within cellular systems as a network, offering vital insights for understanding cell processes and molecular interactions that ...
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 ...
Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges ...
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 ...
Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...
Finding individual-level data for adequatelypowered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large ...
Dublin, Oct. 08, 2025 (GLOBE NEWSWIRE) -- The "Causal AI Market Industry Trends and Global Forecasts to 2035: Distribution by Type of Offering, Deployment Mode, Services, Analytics, Technology, ...
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