What I was trying to do, was:
1. to propose a question that needs an experiment to answer (a hypothesis); and to use that question to make clear that the implicit thought people jump to thinking when reading a headline like this is also "a question that needs an experiment to answer" (a hypothesis.)
2. to propose that when you encounter a headline like this, you should do the same — generate alternative hypotheses, in order to remind yourself that the first interpretation you jumped to of the study's correlation was also just a question.
The point of differentiating the two, is that hypotheses, by themselves, do not convey information about the world. You should not update your mental model of the world based solely on the fact that you generated a plausible-sounding hypothesis. A hypothesis is a question, not an answer.
Certain facts about the world are contingent on a given hypothesis proving out. By privileging a hypothesis — thinking about it and no other — you may implicitly raise your expectations of those contingent facts being true. And that can lead you astray. Those facts haven't earned a place in your mental model of the world. They're not paying rent. They're freeloaders. And it's potentially dangerously false to allow yourself to believe them.
The point of generating other hypotheses isn't to find one that could be more true. It's to scrub those contingent-facts of your intuitively-privileged hypothesis from your mind, by throwing other contingent worlds at it — hopefully many such contingent worlds — until your brain just gives up and avoids absorbing any of those contingent facts, treating them all as they should be treated: as mental noise, unworthy of more than a moment's consideration.
Or, to put that another way:
When there's evidence in favor of two things being somehow connected, but nothing has been proven about the way in which they're connected, the only rational thing to believe about that connection... is nothing. :)
I would argue that making these causal inferences without formal proof is actually somewhat essential and desirable, so long as assumptions and uncertainty are recognized, and that they lead to further probing (informed by such inference).
Many people seem to think science is about "finding the truth" (ie.: obtaining "first-order" information about the world), and that's certainly one way to see it... But I think it's more interesting to think of science as being all about finding, quantifying and describing uncertainty.
This might seem pedantic, but you don't need to abandon all uncertainty in order to make causal inference. I think we're saying mostly the same things, in different ways and with different implicit assumptions about what it means to do science. It's obvious from your answer that you also understand all of this very well.
PS.: I have an MSc in biochem: not claiming to be a great scientist, but I do have formal training so I'm at least familiar with the matter :)
1. generate a good few hypotheses;
2. rank them by their seeming plausibility;
3. discard the ones below some cut-off of "being worth the mental effort";
4. and then try to construct isolated contingent mental models of each of the worlds following from each of the hypotheses being true, making sure to store them in your mind as contingent models, rather than as "facts-in-the-world."
You can reason with inferences over contingent facts, but—especially in situations where several of the hypotheses you generate are equally plausible—it's very useful to sort of mentally "tag" those contingent-facts as contingent, so that you'll realize when you're using them in your reasoning; back up; and "drive down all the roads" (i.e. work out what the answer would be under all the contingent models you're holding onto) instead of just one.
However, if after some actual effort there's only one plausible hypothesis you can think of, then sure, just update on it directly. If there's only one contingent world, keeping it tagged as "contingent" in your mental model isn't doing any useful work for you. You can just learn it, and then unlearn it later if it's not true. (And, of course, that comes up all the time in regular life. Some things really are just "predictable.")
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In domains that are about resolving uncertainty — like scientific research — I would say it's pretty unlikely that you'll ever run into an interesting hypothesis (i.e. the kind you get a grant to study) that is so plausible that its alternatives — or even its null hypothesis! — can be entirely mentally discarded in advance of doing the experiment.
But, on the other hand, this doesn't matter so much; science is nice because it actually is quite tolerant of its participants' mental models being all over the place! "Scientific rigor" is externalized to the scientific process (enforced by peer review) — sort of like rigor in programming can be externalized to the language, and enforced by the compiler. There doesn't need to be much of anything happening within the minds of the researchers. (Thus incrementalism, scientific positivism, etc.)
But this isn't true once you leave the realm of process rigor, and enter the realm of regular people deciding what they should do when they read about scientific studies: how they should—or shouldn't!—seek to apply the "potential facts" they hear about from these studies in their everyday lives.
This is especially relevant in areas where non-scientists are closely following — and attempting to operationalize — the cutting-edge of scientific research, where there is not enough aggregate evidence to prove much. In such domains, it's the consumer of the science that needs good epistemic hygiene, not the scientists themselves. (Good examples of such areas: nootropics; sports nutrition; macroeconomics; and, amusingly, software engineering.)
People in general point to scientific evidence only when it backs their stake, or worse, world view.
As soon as scientific evidence is shown that counters their stake or world view, then we go straight to "correlation is not causation."
I like to think people are more fair than that.
In this case in particular I tend to eat as much spicy food as I can. I have about 20 bottles of hot sauce within arms reach as I type this, I add some set of peppers onions and garlic to basically any meal I cook, and I usually order thai hot when it's offered even though it definitely kicks my ass.
And yet when I read this headline I immediately jumped to correlation.
The principle behind modifying the gut microbiome to achieve good outcomes requires the intestinal flora to change: that means, for example, that some bacteria must die and some bacteria must proliferate. This is one of the main reasons why curcumin (from turmeric), for example, is the subject of much research in the field.
Anti-microbial properties don't apply equally to all species of microbes, and such selective action is essential for modulating the microbiome.
Disturbances of the gut flora do tend to lead to poorer outcomes more often than not, but this is not a hard rule. It's rather that complex machines are fragile, and most modifications done in flight lead to adverse outcomes: without prior knowledge and generally speaking, it's a bit like adding or removing a random line of code in a large application.
Fungi tend to compete with bacteria; if modern lifestyles generally tend to result in a overly fungus-heavy gut biome, a food with anti-fungal properties could restore healthy balance.
There's a good reason to think that spicy food improves outcomes causally from a neurological point of view as well as, possibly, a gut microbiome point of view. I would say that the alternative you describe is mechanistically possible but not as plausible.