David Reber

I'd be interested in seeing other matrix factorizations explored as well. Specifically, I would recommend trying nonnegative matrix factorizations: to quote the Wikipedia article:

This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered.

The added constraint may help eliminate spurious patterns: for instance, I suspect the positive/negative singular value distinction might be a red herring (based on past projects I've worked on).

[Warning: "cyclic" overload. I think in this post it's referring to the dynamical systems definition, i.e. variables reattain the same state later in time. I'm referring to Pearl's causality definition: variable X is functionally dependent on variable Y, which is itself functionally dependent on variable X.]

Turns out Chaos is not Linear...

I think the bigger point (which is unaddressed here) is that chaos can't arise for acyclic causal models (SCMs). Chaos can only arise when there is feedback between the variables right? Hence the characterization of chaos is that orbits of all periods are present in the system: you can't have an orbit at all without functional feedback. The linear approximations post is working on an acyclic Bayes net.

I believe this sort of phenomenon [ chaos ] plays a

central role in abstraction in practice: the “natural abstraction” is a summary of exactly the information whichisn’twiped out. So, my methods definitely needed to handle chaos.

Not all useful systems in the world are chaotic. And the Telephone Theorem doesn't rely on chaos as the mechanism for information loss. So it seems too strong to say "my methods definitely need to handle chaos". Surely there are useful footholds in between the extremes of "acyclic + linear" to "cyclic + chaos": for instance, "cyclic + linear".

At any rate, Foundations of Structural Causal Models with Cycles and Latent Variables could provide a good starting point for cyclic causal models (also called structural equation models). There are other formalisms as well but I'm preferential towards this because of how closely it matches Pearl.

As I understand it, the proof in the appendix only assumes we're working with Bayes nets (so just factorizations of probability distributions). That is, no assumption is made that the graphs are causal in nature (they're not necessarily assumed to be the causal diagrams of SCMs) although of course the arguments still port over if we make that stronger assumption.

Is that correct?

I've been reconsidering the coin run example as well recently from a causal perspective, and your articulation helped me crystalize my thoughts. Building on these points above, it seems clear that the core issue is one of causal confusion: that is, the true causal model M is "move right" -> "get the coin" -> "get reward". However, if the variable of "did you get the coin" is effectively latent (because the model selection doesn't discriminate on this variable) then the causal model M is indistinguishable from M' which is "move right" -> "get reward" (which though it is not the true causal model governing the system, generates the same observational distribution).

In fact, the incorrect model M' actually has shorter description length, so it may be that here there is a bias against learning the true causal model. If so, I believe we have a compelling explanation for the coin runner phenomenon which does

notrequire the existence of a mesa optimizer, and whichdoesindicate we should be more concerned about causal confusion.