What courses of study and applied methods could further computational social science? What are some good resources for foundations in the requisite methods and their impacts?
And then maybe something like,
"Quantum statistical foundations for causal analysis of emergent patterns in adaptive, nonlinear, possibly complex systems"
Though already suggested is:
"Validated Evidence Based Computational Thinking and Cybernetics" https://twitter.com/westurner/status/1118822798217101313
Causal inference > Approaches in social sciences https://en.wikipedia.org/wiki/Causal_inference#Approaches_in...
"Answering causal questions using observational data" (2021) [PDF] https://www.nobelprize.org/uploads/2021/10/advanced-economic... https://www.nobelprize.org/prizes/economic-sciences/2021/pop...
/? causal inference site:github.com awesome https://www.google.com/search?q=awesome%2Bcausal%2Binference...
/? causal inference from: https://westurner.github.io/hnlog/ :
https://news.ycombinator.com/item?id=20178068 ; Python packages for causal inference
"The limits of graphical causal discovery" (2021) https://towardsdatascience.com/the-limits-of-graphical-causa... :
> Structural causal models support three key operations: observations, interventions and counterfactuals.
What is the difference between counterfactuals in structural casual models and counterfactuals in (quantum) Constructor Theory?
How can causal inference identify causal linkages between nonlinear quantum complex adaptive systems (that are affected by other [super-]fluidic fields), in computational social science and data science?