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We're comparing two competing hypotheses to explain the correlation between smoking and cancer. Like I said, we can always introduce new ad-hoc hypotheses, but

We're comparing two competing hypotheses to explain the correlation between smoking and cancer. Like I said, we can always introduce new ad-hoc hypotheses, but good science isn't done willy nilly, it's done with background knowledge of how the systems might plausibly work. The two hypotheses are: (1) smoking → tar → cancer (2) smoking ← genes → cancer Note that (1) contains the sub-hypothesis (A) smoking → tar, which is also compatible with (2). The super-graph containing both graphs is: ————genes———— ↓ ↓ smoking → tar → cancer In that super-graph, there is only one possible explanation for a correlation between smoking and tar: smoking causing tar. So if we see that correlation, then barring alternative hypotheses, we know the causation smoking → tar is true. (Again, we could always hypothesize other causal explanations, but we're presuming no one has yet submitted a plausible alternative explanation.) Now once smoking → tar is established, we are still left with two possible explanations for the observed correlation between smoking and cancer: (1) smoking → tar → cancer (2+A) tar ← smoking ← genes → cancer This is where controlling for smoking behavior comes in. Once we do that, we've severed the indirect statistical link between tar and cancer in graph (2+A). Thus if a correlation still persists between tar and cancer after this statistical control, we've ruled out (2+A), leaving only (1).

Geminidog 6y ago
Right I understand we're introducing new hypothesis, but it still stands. By controlling the causative factor no new hypothesis can undermine an established causation assuming that there are no side effects when we trigger a causation event. Think about it. This means that causation is actually established versus the other method which is a flimsier verification. Another way to look at it is that rather then introducing a new "hypothesis" you had to introduce assumptions about causation. You're thinking in terms of math where you can control your primitives and set a domain and range. In science none of these can be fully controlled and a "new hypothesis" does indeed apply. Think about it. If I introduce a new hypothesis in the real world that actually lends good evidence in support of say the cancer outcome I cannot just discount it and say it's not good science. You can only restrict your universe like this in the world of math and logic, in the real world you have to except any reasonable evidence that comes your way. In practice however, most scientists make a bunch of assumptions and will likely test things using your method because it's just 100x easier and more realistic in terms of setting up an experiment that's possible. Also I mentioned in another comment that your experiment does not actually rule out the genetic factor. Both genetics and smoking and tar could all logically be causative factors for cancer. If the previous statement was true all your correlations established in your post would be exactly the same... there were many assumptions made. So in short your experimental evidence lends support for multiple conclusions that cannot be discounted.
- DavidSJ 6y ago
  The comment you’re replying to addressed your concern about the genetic factor. As I said, by controlling for smoking behavior, you sever the statistical link between tar deposits and genetics in hypothesis (2+A), hence you also sever the indirect statistical link between tar deposits and cancer which is due to genetics.
  - Geminidog 6y ago
    I feel you are conflating hypothesis with logic and science your previous statement of saying: "we can always introduce new ad-hoc hypotheses, but good science isn't done willy nilly," Good science isn't restricting the entire universe to two possible hypothesis. Think about it logically. What scientist can publish a paper that says that genes don't predispose anyone to cancer or smoking because he just observed smoking correlates with tar? You literally say smoking must cause tar because it's the only possibility in your two hypothesis... one must occur. Sure if this was math and you defined your universe to be one of two possibilities... but no person in the real world will take that seriously. In math you don't introduce ad-hoc axioms and rules to your formal analysis, but the scientific method exists because all observation in the real universe is ad-hoc. Anything goes and that's why the best way to make sense of it is through correlation. Which brings me full circle back to my original method. Can you introduce any "adhoc hypothesis" to render a causation established by directly controlling the causative event invalid? You can't. Therefore the actual formal scientific way to determining causation is by placing yourself in the system. The only way the graph method can be applied to the real world is if you map out every possible scenario and assumption into that graph. Then you can say based off of what we know and assume A must cause B. But more often then not a scientist is likely just establishing some correlations and coming to a qualitative conclusion that takes into account all the assumptions and hypothesis that we're aware about in the real world.
    - DavidSJ 6y ago
      The smoking → tar causation is a red herring here. It's presumed it's not controversial. The important point is we're able to discover a tar → cancer causation by controlling for smoking behavior without doing an intervention, and combining this with the smoking → tar causation, we can then reason there's a smoking → cancer causation. Of course this is a simplified example to illustrate the causal logic at play. No one seriously believes there are only ever two possible hypotheses. But the point is the universe of plausible hypotheses is not infinite, either. Scientists in the real world are not dealing with the set of logically possible statements consistent with raw sensory experience. They're building models of the world, and testing and challenging a few of their beliefs at a time, to gradually come closer to the truth. When they can conduct randomized controlled trials, that's great. But it's not always feasible or even possible, yet it's still often possible in those cases to test competing causal hypotheses via observation. The Eddington experiment tested the (causal) theory of General Relativity against the (causal) theory of Newtonian gravity, but there was no intervention. The discovery of iridium in a giant crater off the coast of Mexico was a key confirmation for the (causal) dinosaur meteor extinction theory, but there was no intervention there either. These were causal hypotheses tested by observation. A scientist is always free to propose an alternative hypothesis, but in the mean time, these were tests of their predictions. The point is RCTs are a special case of a more general causal logic. RCTs work because they sever the statistical link to confounders, but only provided your assumptions about the RCTs are correct. In particular, your random number generator must not itself be confounded with what you're measuring (how do you know there's not a hidden variable simultaneously controlling your coin flip and the outcome of your experiment?), you must do the observations double-blinded, your results must not be a statistical fluke, etc. etc. All of these assumptions can be challenged, just like the assumptions in my smoking/lung cancer example. In the real world scientists often accept, at least tentatively, that these assumptions hold. For if they don't, they can never learn anything.

Geminidog 6y ago

Right I understand we're introducing new hypothesis, but it still stands. By controlling the causative factor no new hypothesis can undermine an established causation assuming that there are no side effects when we trigger a causation event. Think about it. This means that causation is actually established versus the other method which is a flimsier verification. Another way to look at it is that rather then introducing a new "hypothesis" you had to introduce assumptions about causation. You're thinking in terms of math where you can control your primitives and set a domain and range. In science none of these can be fully controlled and a "new hypothesis" does indeed apply. Think about it. If I introduce a new hypothesis in the real world that actually lends good evidence in support of say the cancer outcome I cannot just discount it and say it's not good science. You can only restrict your universe like this in the world of math and logic, in the real world you have to except any reasonable evidence that comes your way. In practice however, most scientists make a bunch of assumptions and will likely test things using your method because it's just 100x easier and more realistic in terms of setting up an experiment that's possible. Also I mentioned in another comment that your experiment does not actually rule out the genetic factor. Both genetics and smoking and tar could all logically be causative factors for cancer. If the previous statement was true all your correlations established in your post would be exactly the same... there were many assumptions made. So in short your experimental evidence lends support for multiple conclusions that cannot be discounted.

DavidSJ 6y ago
The comment you’re replying to addressed your concern about the genetic factor. As I said, by controlling for smoking behavior, you sever the statistical link between tar deposits and genetics in hypothesis (2+A), hence you also sever the indirect statistical link between tar deposits and cancer which is due to genetics.
- Geminidog 6y ago
  I feel you are conflating hypothesis with logic and science your previous statement of saying: "we can always introduce new ad-hoc hypotheses, but good science isn't done willy nilly," Good science isn't restricting the entire universe to two possible hypothesis. Think about it logically. What scientist can publish a paper that says that genes don't predispose anyone to cancer or smoking because he just observed smoking correlates with tar? You literally say smoking must cause tar because it's the only possibility in your two hypothesis... one must occur. Sure if this was math and you defined your universe to be one of two possibilities... but no person in the real world will take that seriously. In math you don't introduce ad-hoc axioms and rules to your formal analysis, but the scientific method exists because all observation in the real universe is ad-hoc. Anything goes and that's why the best way to make sense of it is through correlation. Which brings me full circle back to my original method. Can you introduce any "adhoc hypothesis" to render a causation established by directly controlling the causative event invalid? You can't. Therefore the actual formal scientific way to determining causation is by placing yourself in the system. The only way the graph method can be applied to the real world is if you map out every possible scenario and assumption into that graph. Then you can say based off of what we know and assume A must cause B. But more often then not a scientist is likely just establishing some correlations and coming to a qualitative conclusion that takes into account all the assumptions and hypothesis that we're aware about in the real world.
  - DavidSJ 6y ago
    The smoking → tar causation is a red herring here. It's presumed it's not controversial. The important point is we're able to discover a tar → cancer causation by controlling for smoking behavior without doing an intervention, and combining this with the smoking → tar causation, we can then reason there's a smoking → cancer causation. Of course this is a simplified example to illustrate the causal logic at play. No one seriously believes there are only ever two possible hypotheses. But the point is the universe of plausible hypotheses is not infinite, either. Scientists in the real world are not dealing with the set of logically possible statements consistent with raw sensory experience. They're building models of the world, and testing and challenging a few of their beliefs at a time, to gradually come closer to the truth. When they can conduct randomized controlled trials, that's great. But it's not always feasible or even possible, yet it's still often possible in those cases to test competing causal hypotheses via observation. The Eddington experiment tested the (causal) theory of General Relativity against the (causal) theory of Newtonian gravity, but there was no intervention. The discovery of iridium in a giant crater off the coast of Mexico was a key confirmation for the (causal) dinosaur meteor extinction theory, but there was no intervention there either. These were causal hypotheses tested by observation. A scientist is always free to propose an alternative hypothesis, but in the mean time, these were tests of their predictions. The point is RCTs are a special case of a more general causal logic. RCTs work because they sever the statistical link to confounders, but only provided your assumptions about the RCTs are correct. In particular, your random number generator must not itself be confounded with what you're measuring (how do you know there's not a hidden variable simultaneously controlling your coin flip and the outcome of your experiment?), you must do the observations double-blinded, your results must not be a statistical fluke, etc. etc. All of these assumptions can be challenged, just like the assumptions in my smoking/lung cancer example. In the real world scientists often accept, at least tentatively, that these assumptions hold. For if they don't, they can never learn anything.