https://www.cell.com/iscience/fulltext/S2589-0042(24)01111-8
https://www.cell.com/iscience/fulltext/S2589-0042(24)01111-8
Archive for the 'SciLit' Category
A Unification of Mediator, Confounder, and Collider Effects – PMC
August 31, 2024https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967310/
MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193. https://doi.org/10.1007/s11121-021-01268-xMacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193.
https://doi.org/10.1007/s11121-021-01268-x
QT:{{”
Third-variable effects are not distinguishable solely by statistical methods. Each third-variable effect can be fit to the same data, and if the relations between the variables are substantial, there will be evidence for each effect. In this sense, the confounder, mediator, and collider models are equivalent, providing an equal representation of the information contained in the data for three variables (Stelzl, 1986). Although mediation, confounding, and collision may equally explain the statistical associations among three variables, they describe different causal relations among those variables. Like much recent research on causal analysis, this paper highlights the centrality of the causal model underlying a research study and the important distinction between the causal model and the statistical model. The appropriate causal model is determined by prior empirical research and theory. The statistical analysis provides estimates for the proposed causal model.
“}}
A Unification of Mediator, Confounder, and Collider Effects – PMC
August 31, 2024https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967310/
MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193. https://doi.org/10.1007/s11121-021-01268-xMacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193.
https://doi.org/10.1007/s11121-021-01268-x
QT:{{”
Third-variable effects are not distinguishable solely by statistical methods. Each third-variable effect can be fit to the same data, and if the relations between the variables are substantial, there will be evidence for each effect. In this sense, the confounder, mediator, and collider models are equivalent, providing an equal representation of the information contained in the data for three variables (Stelzl, 1986). Although mediation, confounding, and collision may equally explain the statistical associations among three variables, they describe different causal relations among those variables. Like much recent research on causal analysis, this paper highlights the centrality of the causal model underlying a research study and the important distinction between the causal model and the statistical model. The appropriate causal model is determined by prior empirical research and theory. The statistical analysis provides estimates for the proposed causal model.
“}}
2302.04265 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
August 11, 2024https://arxiv.org/abs/2302.04265
thought this was interesting
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., & Jaakkola, T. (2023, February 8). PFGM++: Unlocking the potential of
Physics-Inspired Generative Models. arXiv.org.
https://arxiv.org/abs/2302.04265
Detecting hallucinations in large language models using semantic entropy | Nature
August 11, 2024https://www.nature.com/articles/s41586-024-07421-0
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models | Bioinformatics | Oxford Academic
July 28, 2024Nallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C. (2023). CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models. Bioinformatics, 39(1).
https://doi.org/10.1093/bioinformatics/btad029
https://academic.oup.com/bioinformatics/article/39/1/btad029/6989624
Induction of social contagion for diverse outcomes in structured experiments in isolated villages | Science
May 3, 2024Genetic predisposition, modifiable lifestyles, and their joint effects on human lifespan: evidence from multiple cohort studies | BMJ Evidence-Based Medicine
April 30, 2024https://ebm.bmj.com/content/early/2024/04/16/bmjebm-2023-112583
QT:{{”
Interesting that “alcohol consumption” didn’t rank in the study’s “top 4” lifestyle features. Wonder if this is because it potentially overlaps somewhat with two of the other features (diet & body shape). “}}
Genetic predisposition, modifiable lifestyles, and their joint effects on human lifespan: evidence from multiple cohort studies FREE
http://orcid.org/0000-0002-2628-7535Zilong Bian1,2, Lijuan Wang1,3, Rong Fan1,2, Jing Sun1, Lili Yu1, Meihong Xu4, Paul R H J Timmers3,5, Xia Shen6, James F Wilson3,5, Evropi Theodoratou3,7, Xifeng Wu1,8, Xue Li1,3
linkstream2 microblog 