[HN Gopher] Revealing causal links in complex systems: New algor...
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Revealing causal links in complex systems: New algorithm shows
hidden influences
Author : wglb
Score : 90 points
Date : 2024-11-05 21:00 UTC (7 days ago)
(HTM) web link (techxplore.com)
(TXT) w3m dump (techxplore.com)
| p00dles wrote:
| Nature Communications paper link:
| https://www.nature.com/articles/s41467-024-53373-4
|
| GitHub link: https://github.com/Computational-Turbulence-
| Group/SURD
| Arech wrote:
| Thanks. This should be the actual submission instead of the
| marketing-speak blah-blah
| passwordoops wrote:
| I'm in your camp, but I've found with my own submissions
| here, marketing-speak gets more engagement than the papers.
|
| So my compromise is to post the PR, but give the paper link
| in the first comment
| BenoitP wrote:
| > 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.
| djoldman wrote:
| https://arxiv.org/pdf/2405.12411
| ta988 wrote:
| The Nature Comm paper is open access too.
| navaed01 wrote:
| Reminds me of Granger causality, which had a lot of hype at the
| time, but not a lot of staying power. (I only read the main
| article, which was v. high level - not the scientific paper )
| JackeJR wrote:
| 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.
| Xcelerate wrote:
| I think convergent cross mapping came out after Granger
| causality. Did that ever go anywhere either?
| phyalow wrote:
| 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.
| youoy wrote:
| From the linked article:
|
| > The method, in the form of an algorithm, takes in data that
| have been collected over time, such as the changing populations
| of different species in a marine environment. From those data,
| the method measures the interactions between every variable in a
| system and estimates the degree to which a change in one variable
| (say, the number of sardines in a region over time) can predict
| the state of another (such as the population of anchovy in the
| same region).
|
| I also read the introduction of the paper. Maybe I misunderstood
| something about causal inference, but I thought from data alone
| one could only infer correlations or associations (in general).
| To talk about "causal" links, I thought you need either to assume
| a particular model of the data generation process, or perform
| some interventions on the system to be able to decide the
| direction of the arrows in the "links" in general.
|
| I'm not saying that the paper is wrong or anything, it looks
| super useful! It's just that one should be careful when
| writing/reading the word "causal".
| kqr wrote:
| 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.
| nabla9 wrote:
| 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.
| youoy wrote:
| I was not aware of the additive noise part. I will have to
| look into that, thanks for the info!
| nimithryn wrote:
| Any citation on the additive random noise?
| abdullahkhalids wrote:
| "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.
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