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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

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
>The smoking → tar causation is a red herring here. It's presumed it's not controversial. You have to mention this explicitly. Because it's also presumed not controversial that genetics can predispose someone to cancer. Additionally if you restrict your universe to two hypothesis but then suddenly I can pick up adhoc "red herrings"/assumptions like this anywhere seems inconsistent with your point. >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. This is factually wrong. We don't know whether plausible hypothesis are finite or infinite. This is the very reason why nothing can actually be proven. The entire reason why nothing can be proven with science is because we can't specify a range and domain for the universe because we simply don't know. Formally in the scientific world your method is flawed. This is what I'm saying. In the mathematical world your assumptions and logic can hold but not in science. I get that your toy example was used for illustration purposes but it's failure to be applicable to the real world is also an illustration of the flaw behind the method itself. I can bring it back to the world of axioms and theorems so . Imagine the real world as a model where there the domain and range is infinite in size and entity types. The only thing that can be assumed is probability and logic. That is what the scientific method is operating in. Therefore your methods have to account for an infinite amount of hypothesis rendering your assumptions and deductions impossible without caveating your conclusion with a limiting assumption. In the logical model above the best you can do to establish causality it by placing yourself into the system. Any other method requires assumptions (which is commonly done). >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. Of course. I'm saying we have limited tools so we tend to jump to conclusions and make assumptions based off of our intuition and understanding of probable outcomes. But this does not discount the fact that these assumptions were made. I am saying that the ONLY way to determine causality to a degree in a universe where the domain and codomain is unknowable is by placing your self into the system and making yourself the causal factor. But this is not how we operate in practice. Clearly many scientists assume causality based only off of correlating evidence do in no part to, like you said, how hard it is to actually create these experiments. >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. Ok we're probably on the same page then. This is exactly what I'm saying. All of our science is founded off of a few foundational axioms/assumptions. The recursive assumption that logic is true, the assumption probability is true and maybe a few more intuitive adhoc foundational assumptions that the random number generator is random and that influencing the causal event does not produce side effects. The scientific method is foundational because it operates within these foundational assumptions and so does determining causality by placing yourself into the system. The "RCT" thing you introduced is not foundational because you must create new assumptions outside of the foundational ones we use for all of science. You make up stuff. Which is fine, but it's no different then using correlation to imply causality. It's the same type of "assumption." We're so against the term correlation does not imply causation but these and similar leaps of assumptions are common place, often done and allow us to come to conclusions without actually proving 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

>The smoking → tar causation is a red herring here. It's presumed it's not controversial. You have to mention this explicitly. Because it's also presumed not controversial that genetics can predispose someone to cancer. Additionally if you restrict your universe to two hypothesis but then suddenly I can pick up adhoc "red herrings"/assumptions like this anywhere seems inconsistent with your point. >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. This is factually wrong. We don't know whether plausible hypothesis are finite or infinite. This is the very reason why nothing can actually be proven. The entire reason why nothing can be proven with science is because we can't specify a range and domain for the universe because we simply don't know. Formally in the scientific world your method is flawed. This is what I'm saying. In the mathematical world your assumptions and logic can hold but not in science. I get that your toy example was used for illustration purposes but it's failure to be applicable to the real world is also an illustration of the flaw behind the method itself. I can bring it back to the world of axioms and theorems so . Imagine the real world as a model where there the domain and range is infinite in size and entity types. The only thing that can be assumed is probability and logic. That is what the scientific method is operating in. Therefore your methods have to account for an infinite amount of hypothesis rendering your assumptions and deductions impossible without caveating your conclusion with a limiting assumption. In the logical model above the best you can do to establish causality it by placing yourself into the system. Any other method requires assumptions (which is commonly done). >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. Of course. I'm saying we have limited tools so we tend to jump to conclusions and make assumptions based off of our intuition and understanding of probable outcomes. But this does not discount the fact that these assumptions were made. I am saying that the ONLY way to determine causality to a degree in a universe where the domain and codomain is unknowable is by placing your self into the system and making yourself the causal factor. But this is not how we operate in practice. Clearly many scientists assume causality based only off of correlating evidence do in no part to, like you said, how hard it is to actually create these experiments. >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. Ok we're probably on the same page then. This is exactly what I'm saying. All of our science is founded off of a few foundational axioms/assumptions. The recursive assumption that logic is true, the assumption probability is true and maybe a few more intuitive adhoc foundational assumptions that the random number generator is random and that influencing the causal event does not produce side effects. The scientific method is foundational because it operates within these foundational assumptions and so does determining causality by placing yourself into the system. The "RCT" thing you introduced is not foundational because you must create new assumptions outside of the foundational ones we use for all of science. You make up stuff. Which is fine, but it's no different then using correlation to imply causality. It's the same type of "assumption." We're so against the term correlation does not imply causation but these and similar leaps of assumptions are common place, often done and allow us to come to conclusions without actually proving anything.