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 ...
ascopubs.org

Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World ...

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 ...
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 ...
JSTOR Daily

Causal Isomorphism and Complementarity: Setting the Record Straight

Snyder (1989) has misrepresented the central characteristic of Bohr's complementarity thesis and has similarly misrepresented our ideas of methodological complementarity and causal isomorphism. Bohr's ...