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Seven basic rules for causal inference

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Re: Seven basic rules for causal inference

#11

Post by BenoitP »

currymj wrote: post #3

Rule 2 (“causation creates correlation”) would be strongly disputed by a lot of people. It relies on the assumption of “faithfulness” which is not discussed until the bottom of the article. This is a very innocent sounding assumption but it’s actually quite strong. In particular it may be violated when there are control systems or strategic agents as part of the system you want to study — which is often the case for…

I'd argue you both could be right. Your comment could lead to a definition of intelligence. Organisms capable of causally influencing deterministic systems to their advantage can be marked as intelligent. The complexity of which would determine the degree of intelligence.

Your point is great in that it pinpoints also the notions of agency scopes. In all the causal DAGs it feels like there are implicit regions: ones where we can influence or not, intervene or not, observe or not, where one is responsible for or not.

An intelligent agent is one capable of modelling a system, influence it, and bias it. Such that it can reach and exploit an existing corner case of it. I talk about a corner case because of entropy and murphy's law. For a given energy, there are way many more unadvantageous states than advantageous one. And the intelligence of a system is the complexity required to wield the entropy reduction of an energy source.

Re: Seven basic rules for causal inference

#13

Post by lordnacho »

Vecr wrote: post #5
lordnacho wrote: post #2

This is brilliant. The whole causal inference thing is something I only came across after university, either I missed it or it is a hole in the curriculum, because it seems incredibly fundamental to our understanding of the world. The thing that made be read into it was a quite interesting sentence from lesswrong, saying that actually the common idea that correlation does not imply causation is wrong. Now it's not wr…

When did you go to university? The terminology here came from Pearl 2000, and it probably took years and years after that to diffuse out.

I thought Pearl was writing from 1984 onwards?

I was at university around the millennium.

Re: Seven basic rules for causal inference

#14

Post by ordu »

apwheele wrote: post #9
currymj wrote: post #3

Rule 2 (“causation creates correlation”) would be strongly disputed by a lot of people. It relies on the assumption of “faithfulness” which is not discussed until the bottom of the article. This is a very innocent sounding assumption but it’s actually quite strong. In particular it may be violated when there are control systems or strategic agents as part of the system you want to study — which is often the case for…

This was my thought as well. I don't like showing the scatterplots in these examples, as "correlation" I think is more associated with the correlation coefficient than the more generic independence that the author means in this scenario. E.g. a U shape in the scatterplot may have a zero correlation coefficient but is not conditionally independent.

From the article:

> NB: Correlated does not mean linearly correlated

> For simplicity, I have used linear correlations in all the example R code. In real life, however, the pattern of correlation/association/mutual information we should expect depends entirely on the functional form of the causal relationships involved.

Re: Seven basic rules for causal inference

#15

Post by fn-mote »

dkga wrote: post #8

I highly suggest this paper here for a more complete view of causality that nests do-calculus (at least in economics): Heckman, JJ and Pinto, R. (2024): “Econometric causality: The central role of thought experiments”, Journal of Econometrics, v.243, n.1-2.

Why should you look this paper up? It argues that certain approaches from statistics and computer science are limited, and (essentially) that economists have a better approach. YMMV, but the criticisms are specific (whether or not you buy the "fix").

From the paper:

> Each of the recent approaches holds value for limited classes of problems. [...] The danger lies in the sole reliance on these tools, which eliminates serious consideration of important policy and interpretation questions. We highlight the flexibility and adaptability of the econometric approach to causality, contrasting it with the limitations of other causal frameworks.

Re: Seven basic rules for causal inference

#16

Post by 082349872349872 »

Can these seven be reduced to three basic rules?

- controlling for a node increases correlation among pairs where both are ancestors

- controlling for a node does not affect (the lack of) correlation among pairs where at least one is categorically unrelated (shares no ancestry with that node)

- controlling for a node decreases correlation among pairs where both are related but at least one is not an ancestor

Re: Seven basic rules for causal inference

#17

Post by bubblyworld »

currymj wrote: post #3

Rule 2 (“causation creates correlation”) would be strongly disputed by a lot of people. It relies on the assumption of “faithfulness” which is not discussed until the bottom of the article. This is a very innocent sounding assumption but it’s actually quite strong. In particular it may be violated when there are control systems or strategic agents as part of the system you want to study — which is often the case for…

For anyone else who went down a rabbit hole - this paper describes the problem control systems present for these methodologies: https://www.sciencedirect.com/science/article/abs/pii/B97801...

(paywalled link, but it's available on a well-known useful website)

Re: Seven basic rules for causal inference

#18

Post by currymj »

apwheele wrote: post #9
currymj wrote: post #3

Rule 2 (“causation creates correlation”) would be strongly disputed by a lot of people. It relies on the assumption of “faithfulness” which is not discussed until the bottom of the article. This is a very innocent sounding assumption but it’s actually quite strong. In particular it may be violated when there are control systems or strategic agents as part of the system you want to study — which is often the case for…

This was my thought as well. I don't like showing the scatterplots in these examples, as "correlation" I think is more associated with the correlation coefficient than the more generic independence that the author means in this scenario. E.g. a U shape in the scatterplot may have a zero correlation coefficient but is not conditionally independent.

This is a separate issue and also a good point. Correlation sometimes means “Pearson’s correlation coefficient” and sometimes means “anything but completely independent” and it’s often unclear. In this context I mean the latter.

Re: Seven basic rules for causal inference

#20

Post by [deleted] »

dkga wrote: post #8

I highly suggest this paper here for a more complete view of causality that nests do-calculus (at least in economics): Heckman, JJ and Pinto, R. (2024): “Econometric causality: The central role of thought experiments”, Journal of Econometrics, v.243, n.1-2.

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