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When correlation is better than causation

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Re: When correlation is better than causation

#31

Post by ahmedelsama »

led76 wrote: post #13
bsder wrote: post #11

Isn't the problem here that things are backward? You don't prove causation, but you can disprove it when you find absence of correlation. Observed correlation suggests causation which allows you to make a prediction. A prediction can be tested. The prediction will either be true or false based upon whether the correlation continues to hold. This is one of the problems with A/B tests--they often don't have causation a…

I don’t think this post is trying to use correlation to prove causation. It’s in effect saying that when you can’t be sure that there is a causal relationship between two things that you can still make some decisions. Perfect is the enemy of good as they say.

Yeah I love this! Well said

Re: When correlation is better than causation

#33

Post by brittanymdavis1 »

Kranar wrote: post #22
bsder wrote: post #11

Isn't the problem here that things are backward? You don't prove causation, but you can disprove it when you find absence of correlation. Observed correlation suggests causation which allows you to make a prediction. A prediction can be tested. The prediction will either be true or false based upon whether the correlation continues to hold. This is one of the problems with A/B tests--they often don't have causation a…

You can neither use correlation to prove causation nor can you use causation to prove correlation. X can cause Y but be uncorrelated to Y, and X can be correlated to Y without being caused by Y.

You can use causation to make a decision… and you can use correlation to make a decision. Each with different implications of course - but depending on the context both can be used to do the same task.

Re: When correlation is better than causation

#34

Post by shoo »

jasonwatkinspdx wrote: post #7
asdfasgasdgasdg wrote: post #2

Tl;Dr: never, but causality is hard to establish much of the time, so sometimes we must do without. To be honest, I don't find this very convincing. Most of the insights seem pretty obvious. Like if you're working from the point of correlating totals across differently sized legs of an experiment, you're starting from a really bad place. Personally, I'm not quite positive that I buy that causation is that hard to est…

I think you'd find Judea Pearl's work interesting.

here's an arbitrary review of Pearl's recent book, the book of why: https://www.ams.org/journals/notices/201907/rnoti-p1093.pdf

Re: When correlation is better than causation

#35

Post by shoo »

asdfasgasdgasdg wrote: post #9
jasonwatkinspdx wrote: post #7

Earlier quoted context omitted.

I think you'd find Judea Pearl's work interesting.

To be honest my understanding of math and statistics is far too basic for me to really approach this guy's work, in all likelihood. I have read some papers in this space, like the original TrueSkill paper[1], and I found them utterly impenetrable. I'm sure with sufficient practice I could learn but there are so many things to spend time on. I love the concepts and I do think that they are fascinating tools for modeli…

A pathway to understanding TrueSkill could be first getting an understanding of factor graphs and Minka's expectation propagation algorithm [1][2]. I only have a superficial understanding of those things, but I think I've got an understanding of things that are close enough to give some idea of a pathway:

To understand Minka's expectation propagation algorithm you might first need to get a little intuition about assumed density filtering. One way to understand assumed density filtering could be to read a few tutorials about hidden Markov models [3] or Kalman filters and try to get a feel for why and when and how people might want to approximate posterior probability distributions. It might be hard to build enough intuition without trying to actually apply the things (implement the algorithms) or prove the theory yourself, and then try to come up with your own ideas for how to improve the algorithms.

I completely agree that there are basically infinitely more things to learn than available lifetime. It helps a lot to have a concrete application or goal in mind: then you can focus on learning the tools and theory that move you closer to the goal, rather than learning bits and pieces of unrelated knowledge that don't connect together in a useful way.

[1] https://tminka.github.io/papers/ep/roadmap.html

[2] Minka's EP slide deck from his PhD defense https://tminka.github.io/papers/ep/defense.pdf

[3] Rabiner wrote a famous HMM tutorial https://courses.physics.illinois.edu/ece417/fa2017/rabiner89...

Re: When correlation is better than causation

#36

Post by kqr »

asdfasgasdgasdg wrote: post #9
jasonwatkinspdx wrote: post #7

Earlier quoted context omitted.

I think you'd find Judea Pearl's work interesting.

To be honest my understanding of math and statistics is far too basic for me to really approach this guy's work, in all likelihood. I have read some papers in this space, like the original TrueSkill paper[1], and I found them utterly impenetrable. I'm sure with sufficient practice I could learn but there are so many things to spend time on. I love the concepts and I do think that they are fascinating tools for modeli…

I can almost guarantee you that if you're on Hacker News, you have the prerequisites needed for Judea Pearl.

I read Causality which I understand is the more technical of their books, and everything was presented surprisingly intuitively. Sure, I had to go over some things twice, but that's to be expected when you learn something new.

If you're worried, start with one of the more pop-aimed books? You'll be fine.

(Pearl did change the way I look at causality and correlation, fundamentally for the better, so I do strongly recommend getting familiar with it. I also liked Willful Ignorance which is sort of one the same theme but also not and takes a wider approach.

Re: When correlation is better than causation

#37

Post by jasonwatkinspdx »

kqr wrote: post #36
asdfasgasdgasdg wrote: post #9

Earlier quoted context omitted.

To be honest my understanding of math and statistics is far too basic for me to really approach this guy's work, in all likelihood. I have read some papers in this space, like the original TrueSkill paper[1], and I found them utterly impenetrable. I'm sure with sufficient practice I could learn but there are so many things to spend time on. I love the concepts and I do think that they are fascinating tools for modeli…

I can almost guarantee you that if you're on Hacker News, you have the prerequisites needed for Judea Pearl. I read Causality which I understand is the more technical of their books, and everything was presented surprisingly intuitively. Sure, I had to go over some things twice, but that's to be expected when you learn something new. If you're worried, start with one of the more pop-aimed books? You'll be fine. (Pear…

I agree with this. Causality doesn't have much in the way of advanced math or statistics, but there is a fairly large volume of material to go through, which is its own challenge.

A lightweight introduction to Pearl's ideas is the epilog of his book, which is also his Turing award lecture. Here's a pdf scan, there's also video of him giving this lecture up on the internet if you prefer: http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf

Re: When correlation is better than causation

#38

Post by elcomet »

bsder wrote: post #11

Isn't the problem here that things are backward? You don't prove causation, but you can disprove it when you find absence of correlation. Observed correlation suggests causation which allows you to make a prediction. A prediction can be tested. The prediction will either be true or false based upon whether the correlation continues to hold. This is one of the problems with A/B tests--they often don't have causation a…

You can prove causation with an experimental study. You take a random population, split it in half and modify something for the first half, then look at the effects.

Re: When correlation is better than causation

#39

Post by yobbo »

wahern wrote: post #4

https://en.wikipedia.org/wiki/Abductive_reasoning > [Abductive reasoning] starts with an observation or set of observations and then seeks the simplest and most likely conclusion from the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in ret…

If you want to talk about bayes, you could think about causation as a prior for correlation. We can measure correlation (variates), and then we can infer something about the causation (independent variables).

"Inference to the best explanation" could mean we accept any explanation regardless of how improbable it is - as long as it best explains the data.

The bayesian idea is that we can learn something about causation if we accept uncertainty and impose "sanity constraints" (priors) on the explanation.

Without knowing the real-world mechanics of Y, we can say something like "setting X to 0.33 will increase Y, with 60% probability." It maybe impossible to learn anything else from the data.

Re: When correlation is better than causation

#40

Post by negativesigma »

melling wrote: post #18
Leftium wrote: post #15

> The reality is that causality is very difficult to prove. Not only does it require a higher level of statistical rigor, it also requires A LOT of carefully collected data. Meaning you will have to wait a long time before you can make any causal claim. Malcolm Gladwell's similar message: https://www.pushkin.fm/episode/burden-of-proof/ - A correlation between mining and lung cancer was discovered in 1918, but wasn't…

That’s no exactly true. I think the NFL has moved past denial https://www.espn.com/nfl/story/_/id/22603654/nfl-doctor-says... https://www.today.com/parents/brett-favre-psa-urges-no-tackl... Whether the game can ever be made safe is another issue.

There’s a lot of evidence pointing to cumulative hits being a larger predictor of CTE than concussions. Unfortunately that’s not a problem the NFL can solve so it’s been swept under the rug.

This raises a broader point about Collinearity and whether correlation is actually actionable when the feedback cycle is long. You could easily be working the problem for 20 years before you ever knew you were wrong.

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