11–20 of 74 posts
This article starts off with a common mistake. It is ok to create the 3 categories: > If I suspect that A→B, and I collect data and establish beyond doubt that A&B correlates r=0.7, how much evidence do I have that A→B? > you can divvy up the possibilities as: 1. A causes B 2. B causes A 3. both A and B are caused by a C So far so good, but here is the problem: > Even if we were guessing at random, you’d expect us to…
Correlation vs causation is not actually a complex mathematical problem. The issue is more philosophical. Bayesian networks are a highly sophisticated and flexible framework for thinking about causation. And yet the essence of Bayesian networks can be captured in much simpler methods like Instrumental Variables or simply regressions with controls. In all cases, the true distribution of observables (which we can estim…
This isn't to say the article posted is of no use. Having a 'graph-like' mental model of how things work is incredibly useful, as most education simplifies real-world problems into a few key issues. Although most non-computer science issues can be reduced successfully using the 80/20 rule in real life, sometimes some problems require us to look at the 100s of contributing factors to allow us to solve the problem we're facing properly.
The more 'graph-like' problem solving becomes acceptable as way to solve issues the better of everyone will be.
This article starts off with a common mistake. It is ok to create the 3 categories: > If I suspect that A→B, and I collect data and establish beyond doubt that A&B correlates r=0.7, how much evidence do I have that A→B? > you can divvy up the possibilities as: 1. A causes B 2. B causes A 3. both A and B are caused by a C So far so good, but here is the problem: > Even if we were guessing at random, you’d expect us to…
Second, when gwern says "you'd expect 33%", he [1] does not mean "the abstract 'we' mathematically expect 33%", but indeed "the generic person-on-the-street has an intuitive belief that we should get 33%". If you check the context I think you'll see this fits.
[1] So far as I know, anyhow.
Disagree with the title. Correlation does imply casuation a lot of the times (especially for simple systems). But not always. Therefore, the caution is not to assume it apriori, but pursue further investigation to confirm or reject it. Even when it is rejected, a lot of those cases result in a third variable being the cause behind the correlated "effect" variables.
Perhaps you should read the article before posting your disagreement. (As well as the several other people who appear to have paragraph-sized responses to 8 words, rather than the actual article.)
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The author doesn't assert that this is true, but it definitely means he's thinking about this quite hard.
Earlier quoted context omitted.
A smarter algorithm would refrain from betting on the coin.
That's great if you know a priori that you cannot predict the outcome of what you are betting with.
Statistics serves as a tool to overcome our cognitive biases. But what if these biases are at the center of learning? Take for example the Gambler's Fallacy where a player believes she can predict the outcome of a coin toss with greater certainty than is possible. Obviously she cannot. But if I had to design a Machine Learning algorithm, I would certainly want it to always assume that a pattern existed. That way, if…
A smarter algorithm would refrain from betting on the coin.
Disagree with the title. Correlation does imply casuation a lot of the times (especially for simple systems). But not always. Therefore, the caution is not to assume it apriori, but pursue further investigation to confirm or reject it. Even when it is rejected, a lot of those cases result in a third variable being the cause behind the correlated "effect" variables.
Even in the context of physical processes, like the biological processes Gwern mentions, the notion of "establishing causality" is much more of a regulatory artifact than anything else.
You might call this the machine learning approach (optimize some objective function, regardless of mechanism) as opposed to the statistics approach (generate some measured claim about a process itself).
Statistics serves as a tool to overcome our cognitive biases. But what if these biases are at the center of learning? Take for example the Gambler's Fallacy where a player believes she can predict the outcome of a coin toss with greater certainty than is possible. Obviously she cannot. But if I had to design a Machine Learning algorithm, I would certainly want it to always assume that a pattern existed. That way, if…
A smarter algorithm would refrain from betting on the coin.