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View Code? Open in Web Editor NEWimplements several causal inference algorithms based on dynamical causality (DC) framework
License: MIT License
implements several causal inference algorithms based on dynamical causality (DC) framework
License: MIT License
Hi there,
I very much appreciated your paper and find the content of this repo very intriguing!
I can't see anywhere where you support different embedding dimensions for different variables (and relatedly, tau values). Is this because it was not necessary for your examples, or because it is methodologically insensible?
Thanks!
@PengTao-HUST Thanks for developing this excellent library. I have a few questions.
In the CCM , do you compute the bi-variate cause and effect ? for example, In the air-pollution example , how do we choose the cause and effect variable ? or it is computed bi-directionally for the timeseries. for example NO2 --> RSP and then RSP ---> NO2 in the airpollution example ?
How do we interpret the following causal scores from the heatmap chart? for instance, RSP to NO2 is .605 , which is the cause here and which is the effect ?
What is the min and max for the scores? 0 to1 or -1 to 1 ?
Thanks in advance.
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