SilasBarta comments on Causality does not imply correlation - Less Wrong

13 Post author: RichardKennaway 08 July 2009 12:52AM

You are viewing a comment permalink. View the original post to see all comments and the full post content.

Comments (54)

You are viewing a single comment's thread.

Comment author: SilasBarta 08 July 2009 01:24:07AM *  4 points [-]

In a further article I will exhibit time series for three variables, A, B, and C, where the joint distribution is multivariate normal, the correlation of A with C is below -0.99, and each has zero correlation with B. ...

And in the current comment section, I'm going to give away the answer, since I've run through the PCT demos. (Sorry, I don't know how to format for spoilers, will edit once I figure out or someone tells me.)

You sure you didn't want to figure out on your own? Okay, here goes. Kennaway is describing a feedback control system: a system that observes a variable's current value and outputs a signal that attempts to bring it back towards a reference value. A is an external disturbance. B is the deviation of the system from the reference value (the error). C is the output of the controller.

The controller C will push in the opposite direction of the disturbance A, so A and C will be about anti-correlated. Their combined effect is to keep B very close to zero with random deviations, so B is uncorrelated with both.

The disturbance and the controller jointly cause the error. So, we have A->B and C->B. The error also causes the controller to output what it does, so B->C. (I assume directed cycles are allowed since there are four possible connections and you said there are 16 possible graphs.)

Together, that's A-->B<-->C

(In other news, Kennaway or pjeby will suggest I'm not giving due attenction to Perceptual Control Theory.)

(Edit: some goofs)

Comment author: RichardKennaway 08 July 2009 12:22:42PM *  0 points [-]

You have read my mind perfectly and understood the demos! But I'll go ahead and make the post anyway, when I have time, because there are some general implications to draw from the disconnect between causality and correlation. Such as, for example, the impossibility of arriving at A-->B<-->C for this example from any existing algorithms for deriving causal structure from statistical information.

Comment author: SilasBarta 08 July 2009 05:21:08PM 2 points [-]

the impossibility of arriving at A-->B<-->C for this example from any existing algorithms for deriving causal structure from statistical information.

Correct me if I'm wrong, but I think I already know the insight behind what you're going to say.

It's this: there is no fully general way to detect all mutual information between variables, because that would be equivalent to being able to compute Kolmogorov complexity (minimum length to output a string), which would in turn be equivalent to solving the Halting problem.

Comment author: RichardKennaway 08 July 2009 08:46:45PM *  0 points [-]

Correct me if I'm wrong

You're wrong. :-)

Kolmogorov complexity will play no part in the exposition.

Comment author: SilasBarta 08 July 2009 10:17:55PM -1 points [-]

Check my comment: I was only guessing the underlying insight behind your future post, not its content.

I obviously leave room for the possibility that you'll present a more limited or more poorly-defended version of what I just stated. ;-)