> 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.

The Illusion of Causality in Charts