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 ...
The aim of this research therefore was to streamline the understanding of typical causal structures in both randomized and nonrandomized clinical trials in oncology, presenting concise guidelines for ...
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 ...
The surge in enterprise AI has fueled interest in causal analysis. In this piece, I explore the threads that bind cause and effect - and how they can be applied across a range of industry scenarios.
To build truly intelligent machines, teach them cause and effect. The formal modeling and logic to support seemingly fundamental causal reasoning has been lacking in data science and AI, a need Pearl ...
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