[FRIAM] PM-2017-MethodologicalBehaviorismCausalChainsandCausalForks(1).pdf

[jon zingale]

In an attempt to ground my above thoughts, and respond to your apprehension,
I found these two John Baez articles offering some explanation for why one
might want to use category theory to guide Bayesian network calculations.
Additionally, I was pleased to see that he makes explicit the connection to
monoidal categories[2].

[1]
https://golem.ph.utexas.edu/category/2018/07/bayesian_networks.html#:~:text=in%20causal%20theory.-,Introduction,nodes%2C%20satisfying%20the%20Markov%20condition.

[2]
https://golem.ph.utexas.edu/category/2018/01/a_categorical_semantics_for_ca.html#more

For my own part, the intuition to approach the problem of separating actual
*causes* from collections of *evidence* via presheaves mostly follows from
choosing to study the epimorphisms associated with such networks. The
sections are going to need to compose, forming *narratives* (not sure what
else to call these) that give _a_ lineage of causes. It strikes me (a
tourist) that such a framework could be useful when reasoning about
evidence.



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