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CauseNet: Towards a causality graph extracted from the web

causenet.org

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Re: CauseNet: Towards a causality graph extracted from the web

#31

Post by vintermann »

tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

As I understand it, this is a dataset of claimed causation. It should contain vaccines->autism, not because it's true, but because someone, in public, claimed that it was.

So, by design, it's pretty useless for finding new, true causes. But maybe it's useful for something else, such as teaching a model what a causal claim is in a deeper sense? Or mapping out causal claims which are related somehow? Or conflicting? Either way, it's about humans, not about ontological truth.

Re: CauseNet: Towards a causality graph extracted from the web

#33

Post by Amadiro »

kolektiv wrote: post #10
thicknavyrain wrote: post #8

I know it's a reductive take to point to a single mistake and act like the whole project might be a bit futile (maybe it's a rarity) but this example in their sample is really quite awful if the idea is to give AI better epistemics: { "causal_relation": { "cause": { "concept": "vaccines" }, "effect": { "concept": "autism" } } }, ... seriously? Then again, they do say these are just "causal beliefs" expressed on the i…

Oh, ouch, yeah. We already know that misinformation tends to get amplified, the last thing we need is a starting point full of harmful misinformation. There are lots of "causal beliefs" on the internet that should have no place in any kind of general dataset.

It's even worse than that, because the way they extract the causal link is just a regex, so

"vaccines > autism"

because

"Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."

I think this could be solved much better by using even a modestly powerful LLM to do the causal extraction... The website claims "an estimated extraction precision of 83% " but I doubt this is an even remotely sensible estimate.

Re: CauseNet: Towards a causality graph extracted from the web

#34

Post by dr_dshiv »

tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Democritus (b 460BCE) said, “I would rather discover one cause than gain the kingdom of Persia,” which suggests that finding true causes is rather difficult.

Re: CauseNet: Towards a causality graph extracted from the web

#36

Post by Nevermark »

koliber wrote: post #17
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Exactly. In some cases disease causes death. In others it causes immunity which in turn causes “good health” and postpones death.

Contradictory cause-effect examples, each backed up with data, are a reliable indicator of a class of situations that need a higher chain-effect resolution.

Which is directly usable knowledge if you are building out a causal graph.

In the meantime, a cause and effect representation isn't limited to only listing one possible effect. A list of alternate disjoint effects, linked to a cause, is also directly usable.

Just as an effect may be linked to different causes. Which if you only know the effect, in a given situation, and are trying to identify cause, is the same problem in reverse time.

Re: CauseNet: Towards a causality graph extracted from the web

#37

Post by morpheuskafka »

vintermann wrote: post #31
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

As I understand it, this is a dataset of claimed causation. It should contain vaccines->autism, not because it's true, but because someone, in public, claimed that it was. So, by design, it's pretty useless for finding new, true causes. But maybe it's useful for something else, such as teaching a model what a causal claim is in a deeper sense? Or mapping out causal claims which are related somehow? Or conflicting? Ei…

Also, it seems to mistake some definitions as causes.

A coronavirus isn't "claimed" to cause SARS. Rather, SARS is a name given to the disease cause by a certain coronavirus. Or alternatively, the name SARS-nCov-1 is the name given to the virus which causes SARS. Whichever way you want to see it.

For a more obvious example, saying "influenza virus causes influenza" is a tautology, not a causal relationship. If influenza virus doesn't cause influenza disease, then there is no such thing as an influenza virus.

Re: CauseNet: Towards a causality graph extracted from the web

#38

Post by Nevermark »

jiggawatts wrote: post #22
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Even more importantly, it's not even a simple probability of death, or a fraction of a cause, or any simple one-dimensional aspect. Even if you can simplify things down to an "arrow", the label isn't a scalar number. At a bare minimum, it's a vector, just like embeddings in LLMs are! Even more importantly, the endpoints of each such causative arrow are also complex, fuzzy things, and are best represented as vectors.…

That was very well said.

One quibble, and really mean only one:

> a high-dimensional probabilistic causal framework

Deep learning models aka neural network type models, are not probabilistic frameworks. While we can measure on the outside a probability of correct answers across the whole training set, or any data set, there is no probabilistic model.

Like a Pachinko game, you can measure statistics about it, but the game itself is topological. As you point out very clearly, these models perform topological transforms, not probabilistic estimations.

This becomes clear when you test them with different subsets of data. It quickly becomes apparent that the probabilities of the training set are only that. Probabilities of the exact training set only. There is no probabilistic carry over to any subset, or for generalization to any new values.

They are estimators, approximators, function/relationship fitters, etc. In contrast to symbolic, hard numerical or logical models. But they are not probabilistic models.

Even when trained to minimize a probabilistic performance function, their internal need to represent things topologically creates a profoundly "opinionated" form of solution, as apposed to being unbiased with respect to the probability measure. The measure never gets internalized.

Re: CauseNet: Towards a causality graph extracted from the web

#40

Post by Xmd5a »

DrScientist wrote: post #29
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

I totally agreed that in the past years of hammering out an ontology for a particular area just results in a common understanding between those who wrote the ontology and a large gulf between them and the people they want to use it ( everyone else ). What's perhaps different is that the machine, via LLM's, can also have an 'opinion' on meaning or correctness. Going fully circle I wonder what would happen if you got L…

>what would happen if you got LLM's to define the ontology.

https://deepsense.ai/resource/ontology-driven-knowledge-grap...

>hammering out an ontology for a particular area just results in a common understanding between those who wrote the ontology and a large gulf between them and the people they want to use it

This is the other side of the bitter lesson, which is just the empirical observation of a phenomenon that was to be expected from first principles (algorithmic information theory): a program of minimal length must get longer if the reality it models becomes more complex.

For ontologists, the complexity of the task increases as the generality is maintained while model precision is increased (top down approach), or conversely, when precision is maintained the "glue" one must add to build up a bigger and bigger whole while keeping it coherent becomes more and more complex (bottom up approach).

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