The BMJ

Causal inference is hard, and advanced statistical analysis is not enough

Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...
JSTOR Daily

Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness

We describe how ordinary interpretations of causal models and causal graphs fail to capture important distinctions among ignorable allocation mechanisms for subject selection or allocation. We ...
Forbes

Three Ways A Causal Approach Can Improve Trust In AI

Bernd Greifeneder is the CTO and Founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. IT, development and business departments are under more ...
News Medical

Advancing gene regulatory network inference with causal discovery and graph neural networks

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 ...
Diginomica

How causal analysis and AI intersect - methods of causal inference

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 ...