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

[Frank Wimberly]

Thanks, Jon.  I will work on this.

One of my first tasks in my causal reasoning job was to write Java classes
and methods and and applet that used them to allow a user to interactively
enter a DAG and then enter pairs of nodes and return the d-separation facts
for each pair.  Chris Meek who is mentioned by Baez defined the task for me.

---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

On Thu, Feb 18, 2021, 9:20 AM jon zingale <jonzingale at gmail.com> wrote:

> 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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