Causal inference in the continuous limit
2021-02-17 — 2021-02-17
algebra
causal
functional analysis
graphical models
how do science
machine learning
networks
neural nets
PDEs
probability
sciml
statistics
stochastic processes
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Where our causality is over fields of continuously indexed variables rather than discrete nodes, what can we do? Do we talk about light cones? Wave propagation? Covariance?
Unclear.
Graphons look like they should help here, except they do not capture what I would typically imagine about causality, e.g. which covariates influence causation. Something which encodes spatial proximity, or temporal ordering, or other covariates, looks more useful at a glance? Presumably, I am behind the literature.
1 References
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