Revealing causal links in complex systems: New algorithm shows hidden influences

You are correct, as far as I know. I'm wondering if there's some sense in which one can infer such a model from conditional correlations.

That's the first order truth. If you don't have any knowledge about the system, you can't infer causality with observations alone.

With some generic assumptions, or prior knowledge about the system, you can do causal discovery.

For example, just the assumption that there is additive random noise enables discovering causal arrows just by observing the system.

"collected over time" is the operational phrase. You should be able to determine causality, at least partially, if you know the change in correlated variables occur at different times.

You can always restrict the meaning of "causality".

E.g. Granger casusality means that A is typically detected before B and not the other way around (so not mere correlation). It's a moby useful concept.

Correlation also assumes model of the data generating process, but you are correct in thinking that talking about causal links imposes even stronger assumption on the model and data structure for making inference. And then further you have to take a very narrow and convenient interpretation of what causality means (e.g. can't be at the actual level of individual samples, can't manifest through cycles or loops in the variables etc), which is even more of a vexing philosophical question than even the thorny questions in classical statistical inference

Thanks. This should be the actual submission instead of the marketing-speak blah-blah

> Decomposition of causality: It decomposes causal interactions into redundant, unique, and synergistic contributions.

Seen elsewhere: https://github.com/BCG-X-Official/facet, which uses SHAP attributions as inputs:

> The SHAP implementation is used to estimate the shapley vectors which FACET then decomposes into synergy, redundancy, and independence vectors.

But FACET it still about sorting things out in the 'correlation world'.

To get back to SURD: IMHO, when talking about causality one should incorporate some kind of precedence, or order; One thing is the cause of another. Here in SURD they sort of introduce it in a roundabout way by using time's order:

> requiring only pairs of past and future events for analysis

But maybe we could have had fully-fledged custom DAGs, like from here https://github.com/nathanwang000/Shapley-Flow (which don't yet have the redundant/unique/synergistic decomposition)

Also, how do we deal with undetectable "post hoc ergo propter hoc" fallacy, though? (travesting time as causal ordering). How do we deal with confounding? Custom DAGs would have been great.

I'm longing for a SURD/SHAP/FACET/Shapleyflow integration paper. We're so close to it.

The Nature Comm paper is open access too.

Granger's causality is a very restrictive and incomplete view of causality. Pearl's counterfactual system with do calculus is a more general way to think about causality. This SURD appears to be a souped up version of Granger.

I think convergent cross mapping came out after Granger causality. Did that ever go anywhere either?

Page 2 speaks about the relationship and precedent of GC... Honestly worth a read, this is definitely one of most interesting papers for me in the last few years.