It's not a problem of interpretation or visualization or charts. People are talking about it as if it's deception or interpretation but the problem is deeper than this.

It's a fundamental problem of reality.

The nature of reality itself prevents us from determining causality from observation, this includes looking at a chart.

If you observe two variables. Whether those random variables correlate or not... there is NO way to determine if one variable is causative to another through observation alone. Any causation in a conclusion from observation alone is in actuality only assumed. Note the key phrase here is: "through observation alone."

In order to determine if one thing "causes" another thing, you have to insert yourself into the experiment. It needs to go beyond observation.

The experimenter needs to turn off the cause and turn on the cause in a random pattern and see whether that changes the correlation. Only through this can one determine causation. If you don't agree with this, think about it a bit.

Also note that this is how they approve and validate medicine... they have to prove that the medicine/procedure "causes" a better outcome and the only way to do this is to actually make giving and withholding the medicine as part of the trial.

I find the definition of causality that places it squarely in the realm of philosophy to be a dead end or perhaps a circle with no end or objective or goal.

"What does it mean that something is caused by something else?" At the end of it all, what matters is how it's used in the real world. Personally I find the philosophical discussion to be tiresome.

In law, "to cause" is pretty strict: "but for" A, B would not exist or have happened. Therefore A caused B. That's one version. Other people and regimes have theirs.

This is why it's something I try to avoid.

In any case, descriptions of distributions are more comprehensive and avoid conclusions.

I'm not talking about philosophy. Clinical trials for medicine use this technique to determine causality. I'm talking about something very practical and well known.

It is literally the basis for medicine. We literally have to have a "hand in the experiment" for clinical trials to with-hold medicine and to give medicine in order to establish that medicine "causes" a "cure". Clinical trials are by design not about just observation.

Likely, you just don't understand what I was saying.

I believe I understood what you were saying.

The criteria or definition for " A causes B" that you alluded to is a useful one in the context of medicine:

> The experimenter needs to turn off the cause and turn on the cause in a random pattern and see whether that changes the correlation. Only through this can one determine causation. If you don't agree with this, think about it a bit.

It's useful because it establishes a threshold we can use and act on in the real world.

I think there is more nuance and context here though. In clinical trials, minimum cohort sizes are required, possibly related or proportional to power analysis (turning on and off the cause for one person doesn't give us much confidence but for 1000 people gives much more).

So the definition of causes for clinical trials and medicine hinges on more than just turning on and off, it relies on effect size and population size in the experiment.

Going back to TFA, this is the problem when we bring "cause" into the discussion: the definition of it varies depending on the context.

> So the definition of causes for clinical trials and medicine hinges on more than just turning on and off, it relies on effect size and population size in the experiment.

Of course. Because the clinical trial is statistical so the basis of the trial is trying to come to a conclusion about a population of people via a sample. That fact applies to both correlation or causation. Statistics is like an extension from person to people… rather then coming to a conclusion about something for one person you can do it for a sample of the population.

Causality is independent of the extension. You can measure causality against a sample of a population or even a single thing. The property of inserting yourself into an experiment still exists in both cases. This is basic and a simple thought experiment can determine this.

You have two switches two lights and two people. Both people turn each of their respective switches on and off and the light turns on and off in the expected pattern exactly like the state of the switch.

You know one of the switches is hooked to the light and “causes” the light to turn on and off. The other switch is BS and is turning on and off on some predetermined pattern and the person that’s flipping the related switch memorized the pattern and is making it look like the switch is causative to turning on or off the light.

How do you determine which one is the switch that is causative to the light turning on and off and which switch isn’t?

Can you do it through observation alone? Can you just watch the people flip the switch? Or do you have to insert yourself into the experiment and flip both switches randomly yourself to see which one is causal to the light turning on or off?

The answer is obvious. I’m sort of anticipating a pedantic response where you just “observe” the wiring of the switch and the light to that I would say I’m obviously not talking about that. You can assume all the wiring is identical and the actual mechanism is a perfect black box. We never actually try to determine causality or correlation unless we are dealing with a black or semi black box so please do not go down that pedantic road.

You should walk away from this conversation with new intuition on how reality works.

You shouldn’t be getting to involved in mathematical definitions and details of what involves a clinical trial or pedantic details and formal definitions.

Gain deep understanding of why causality must be determined this way and then that helps you see why the specific detailed protocols of clinical trials were designed that way in the first place.

I'd say this is generally true, but in practice there are a decent number of cases where some reasoning can give you a pretty good confidence one way or another. Mainly by considering what other correlations exist and what causal relationships are plausible (because not all of them are).

(I say this coming from an engineering context, where e.g. you can pretty confidently say that your sensor isn't affecting the weather but vice-versa is plausible)

This is true fundamentally. It is not general. It is a fundamental facet of reality.

In practice it’s hard to determine causality so people make assumptions. Most conclusions are like that. I said this in the original post that conclusions from observation alone must have assumptions made. Which is fine given available resources. If you find people who smoke weed have lower iq you can come to the conclusion that weed causes iq to lower assuming that all smokers of weed had average iq before smoking and this is fine.

I’m sure you’ve seen many causative conclusions redacted because of incorrect assumptions so it is in general a very unreliable method.

And that’s why in medicine they strictly have to do causative based testing because they can’t afford to have a conclusion based off of an incorrect assumption.

Sorry, I meant in general in the broader sense that you were intending (i.e., I agree). And if you really get down to brass tacks, it's not obvious you can actually do truly causative based tasting. (See e.g. superdeterminism, which posits that you fundamentally can't as a way to explain quantum weirdness in physics).