Seven basic rules for causal inference
pedermisager.org
Seven basic rules for causal inference
1–10 of 71 posts
Re: Seven basic rules for causal inference
#2This 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 wrong in the face-value sense, it's wrong in the sense that actually you can use correlations to learn something about causation, and there turns out to be a whole field of study here.
Re: Seven basic rules for causal inference
#3Rule 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 causal inference. In such scenarios (eg the famous thermostat example) you could have strong causal links which are invisible in the data.
Re: Seven basic rules for causal inference
#4Are the assumptions "No spurious correlation", "Consistency", and "Exchangeability" ever actually true? If a dataset's big enough you should generally be able to find at least one weird correlation, and the others are limits of doing statistics in the real world.
Re: Seven basic rules for causal inference
#5This 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.
Re: Seven basic rules for causal inference
#6This is missing my favourite rule.
0. The directions of all arrows not part of a collider are statistically meaningless.
Re: Seven basic rules for causal inference
#7This is missing my favourite rule. 0. The directions of all arrows not part of a collider are statistically meaningless.
What's not part of a collider? Good luck with your memory in that case.
Re: Seven basic rules for causal inference
#8I 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.
Re: Seven basic rules for causal inference
#9Rule 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.
Re: Seven basic rules for causal inference
#10Rule 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.
> E.g. a U shape in the scatterplot may have a zero correlation coefficient but is not conditionally independent.
Ok this is correct, but has nothing to do with causality. Whether or not two variables are correlated and whether or not they are independent, and when one does or doesn't imply the other, is a conversation that can be had without resorting to the concept of causality at all. And in fact that's how the subject is taught at an introductory level basically 100% of the times.