pip install -U git+https://github.com/jgreathouse9/mlsynth.git
đź”— Read the full post here:
https://jgreathouse9.github.io/docs/scmo.html
pip install -U git+https://github.com/jgreathouse9/mlsynth.git
đź”— Read the full post here:
https://jgreathouse9.github.io/docs/scmo.html
#simplicialcomplex + #Causality +#Reservoircomputing:
"Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction" https://www.nature.com/articles/s41467-024-46852-1
For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.
Today I came across a post on LinkedIn by a digital specialist. He confidently claimed that with an A/B test, we can determine not just correlation, but true causality. He used words like “certainty” as if statistics were part of Newtonian physics — clear, absolute, unquestionable. I’m surprised by that level of confidence. I don’t have it.
'Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables', by Wei Jin, Yang Ni, Amanda B. Spence, Leah H. Rubin, Yanxun Xu.