4 ms·

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 b

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.
  - DoctorOetker 6y ago
    > 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. Absolutely. Science is about using mathematics and logic to abductively reason about and tease out underlying facts. In fact I see provaility in science and causation as orthogonal issues: Consider a stable supplied voltage and a variable voltage divider (potentiometer). Suppose measuring the voltage once has intrinsic RMS voltage noise associated with it. So while we can never prove experimentally the exact value of the voltage at the voltage divider with zero error, we can drive the error arbitrarily low by oversampling, which means repeating the experiment over and over. In the general case we never actually reach the true value, but that doesn't mean "voltage can not be measured". Trying to measure this voltage inappropriately (say outside the reference range of an ADC, or with an ADC with too low a resolution such that the noise is systematically rounded in the same direction doesn't mean it's impossible to measure voltage, just that the measurement attempt is flawed. Similar with causation. Of course caution is always advised with naive correlation. The problem is not propagation of such caution, but the pretense that no techniques exist to gather evidence of causation, and the omission of such techniques when sharing such a rant.
    - Geminidog 6y ago
      >we can drive the error arbitrarily low by oversampling, which means repeating the experiment over and over. Sure, this operates on the assumption that all potential subsequent experiments that you could have conducted after your final observation also falls within your expected parameters. You did 500 experiments that fit within your hypothesis but the next 10 trillion experiments you didn't do afterwords could have shown wildly different results rendering your 500 experiments as simply "noise." This is a possibility and illustrates the flaw within the scientific method itself, it is however the best that we have, so we use the scientific method and its associated assumptions. Keep in mind the formal result of science has this flaw embedded within the correlation or causation number. However the qualitative conclusion is littered with assumptions. Probably the best way to describe our different view points is optimism vs. pessimism. That's really it. I'm saying it's flawed but it's the best we have, you're saying it's been remarkably successful "so far."

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.
- DoctorOetker 6y ago
  > 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. Absolutely. Science is about using mathematics and logic to abductively reason about and tease out underlying facts. In fact I see provaility in science and causation as orthogonal issues: Consider a stable supplied voltage and a variable voltage divider (potentiometer). Suppose measuring the voltage once has intrinsic RMS voltage noise associated with it. So while we can never prove experimentally the exact value of the voltage at the voltage divider with zero error, we can drive the error arbitrarily low by oversampling, which means repeating the experiment over and over. In the general case we never actually reach the true value, but that doesn't mean "voltage can not be measured". Trying to measure this voltage inappropriately (say outside the reference range of an ADC, or with an ADC with too low a resolution such that the noise is systematically rounded in the same direction doesn't mean it's impossible to measure voltage, just that the measurement attempt is flawed. Similar with causation. Of course caution is always advised with naive correlation. The problem is not propagation of such caution, but the pretense that no techniques exist to gather evidence of causation, and the omission of such techniques when sharing such a rant.
  - Geminidog 6y ago
    >we can drive the error arbitrarily low by oversampling, which means repeating the experiment over and over. Sure, this operates on the assumption that all potential subsequent experiments that you could have conducted after your final observation also falls within your expected parameters. You did 500 experiments that fit within your hypothesis but the next 10 trillion experiments you didn't do afterwords could have shown wildly different results rendering your 500 experiments as simply "noise." This is a possibility and illustrates the flaw within the scientific method itself, it is however the best that we have, so we use the scientific method and its associated assumptions. Keep in mind the formal result of science has this flaw embedded within the correlation or causation number. However the qualitative conclusion is littered with assumptions. Probably the best way to describe our different view points is optimism vs. pessimism. That's really it. I'm saying it's flawed but it's the best we have, you're saying it's been remarkably successful "so far."