4 ms·

I can't see the full study methodology without an account. That said… The challenge with running studies ethically is that you can't split your test groups int

I can't see the full study methodology without an account. That said… The challenge with running studies ethically is that you can't split your test groups into two groups and somehow induce cannabis use disorder and see if major depression, bipolar, etc results from it. Rather, you can see if the people who naturally develop the disorder also have those problems. The flaw in this reasoning is that people who develop depression and similar issues are likely to self-medicate with cannabis. In related news, umbrellas do not cause rain.

l33t7332273 3y ago
How is correlation vs causation determined in other studies where a control group cannot be used?
- deleted 3y ago
  [deleted]
- PheonixPharts 3y ago
  It's important to recognize that we've never successfully answered Hume's general skepticism about the existence of causality as something that is real outside of our own minds. So causality can't be positively confirmed to exist at all, let alone be detected and confirmed through statistics. Before you dismiss that is philosophical non-sense, causality is closely related to the "Arrow of Time" which is considered an unsolved problem in physics [0]. From what we've observed time appears to be the only asymmetric physical process, largely due to entropy (that is you can immediately tell if a video of a jar breaking is being played backwards because we don't expected a jar to "fall together"). There are Quantum processes, for example, that are time symmetric, that is playing a video of these processes would look the same reversed and forward. That said, as someone who does a lot of statistics, in practice what we do is model the causal process with a directed acyclic graph and see how well our models behave under causal assumptions. These work okay for answering practical questions about does A cause B. By controlling for correlating variables we can see the impact on what we believe to be a causal variable when we condition on these other correlating factors. Worth mention that nearly all of the work is done by linear models in practice. 0. https://en.wikipedia.org/wiki/Arrow_of_time https://en.wikipedia.org/wiki/Arrow_of_time
  - xpe 3y ago
    The comment above refers to Hume's 'induction problem' [1]. > we've never successfully answered Hume's general skepticism about the existence of causality as something that is real outside of our own minds The quote seems to imply a strong consensus, but [1] disagrees. The phrasing "the existence of causality as something that is real outside of our own minds" sounds like philosophical idealism [2]. One can take that view without discussing causation at all, seems to me. Forgive the following blunt questions: how is philosophical idealism either (a) useful or (b) testable? To me, currently, it seems to be neither. Maybe I'm missing something? For those that get value from it, what are some practical take-aways you get out of thinking about Hume's 'problem of induction'? I'm very much into philosophy, but I admit I don't see a lot of utility in digging into this outside of historical purposes. It feels to me that the tools from inferential statistics combined with some deeper study on causation are far more useful and enlightening than trying to figure out the historical context around Hume's writing here. I'm glad other people do it, for sure, but what I've skimmed doesn't "grab" me. WRT causation, I often return to the idea of "no model is perfect, but some are useful". There are many times when we build useful models that -- when combined with proper experimentation -- very strongly indicate causation. The laws of physics, for example. As soon as people get involved, it gets messy. [1]: https://plato.stanford.edu/entries/induction-problem/ https://plato.stanford.edu/entries/induction-problem/ [2]: https://plato.stanford.edu/entries/idealism/ https://plato.stanford.edu/entries/idealism/
    - brutusborn 3y ago
      I completely agree that looking at old epistemology isn't that useful; simply because more recent theorists will need to address the same issues, so generally refer to older theory within their work. If you want a deep understanding then you need to look at the source material, but as you mention this is mostly only interesting for gaining a historical perspective. Unfortunately many of the people who create introductory science courses seem to mostly only be aware of Popper, so most students are aware of the importance of falsifiability but not of its problems. I suspect this is because reading Kuhn, Lakatos and Feyerabend leaves one with an impression that science isn't an objective endeavour and this is an uncomfortable truth when start from a belief that science is some infallible rationalist project. Personally it transformed my view from "trust the science"(TM) to "question the science, always." Science isn't always science unfortunately.
- sacrosancty 3y ago
  [dead]
labrador 3y ago
Don't bother trying to read the full text. They ask for all your information then you still can't read it.
nuclearnice3 3y ago
Indeed. Your reasoning was echoed in this critique from Prof David Curtis, Honorary Professor, UCL Genetics Institute. > “Around 20% of young people in Denmark use cannabis but in this study fewer than 1% of the sample had been assigned a diagnosis of cannabis use disorder and these may well have been people who had a pre-existing susceptibility to depression and other psychiatric disorders. > “Other studies have shown that cannabis use itself is not associated with increased risk of depression, so perhaps we are seeing that people with a pre-existing susceptibility to depression are more likely to be diagnosed with cannabis use disorder. This clearly does not mean that we should infer that cannabis somehow causes depression. > “I don’t think that this study provides us with much information to help decide the extent to which cannabis use is, or is not, harmful.” [1] https://www.sciencemediacentre.org/expert-reaction-to-study-of-cannabis-use-disorder-bipolar-disorder-and-depression/ https://www.sciencemediacentre.org/expert-reaction-to-study-...
taurath 3y ago
This precisely. Who’s going to utilize weed more? People going through mental anguish. You know what causes major depression anxiety and psychosis? Trauma. Read the ACE study. A cruel traumatizing environment and behaviors are far more to blame than cannabis.
avodonosov 3y ago
Isn't it possible to distinguish casuality between A and B from correlation by simply considering set sizes? !A & B A & !B A & B !A & !B A B !A !B
- pseudocomposer 3y ago
  Could you explain which distributions you think are correlation and which are causation?
  - avodonosov 3y ago
    I hoped someone who knows statistics will just tell us, instead of me stretching my brain. The famous phrase "correlation does not mean casuation" is not equal to "correlation is indistingushable from casuation", but that's how people seem to often treat it. Ideally, the paper abstract should be clear about what can or can not be concluded. In the title they say "subsequent" as if suggesting casuality, but in the abstract they say "associated". Confusing for a layman such as me. My intuition of how to distinguish correlation from casuation: If cannabis and depression were symmetrically connected, then incease of chances for cannabis use between depressed was equal to the increase of chances for depression between cannabis users. If cannabis increases chances for depression more that depression increases chances for cannabis, than we conclude cannabis tends to cause depression. Formula: percentage of depressed between canabis users / percentage of depressed in general population > persentage of cannabis users between depressed / persentage of cannabis users in general population. Something like that.
    - avodonosov 3y ago
      He-he, the sides of inequality in my formula are always equal :) (c&d/c)/(d/total) > (c&d/d)/(c/total) c&d*total/c*d > c&d*total/d*c
      - avodonosov 3y ago
        Looking at it further, it's indeed not so easy. If we have a statistics produced by some casual relationship, e.g. function takeMeasurement() { // c (the cause) itself has probability 0.5 // d (the dependent) in absence of c has probability 0.5 // but the presence of c increases it to 0.7 const c = Math.random(1.0) < 0.5; const d = Math.random(1.0) < (c ? 0.7 : 0.5); return {c, d}; } one can calculate an equally plausible hypothesis of causality in opposite direction, which would produce an equal statistics: P(c) = 0.5 P(d|!c) = 0.5 P(d|c) = 0.7 P(d) = ? P(c|!d) = ? P(c|d) = ? ------------------------------- P(d) = P(c)*P(d|c) + P(!c)*P(d|!c) = = P(c)*P(d|c) + (1 - P(c))*P(d|!c) = = 0.5*0.7 + 0.5*0.5 = 0.35 + 0.25 = = 0.6 P(c|!d) = P(c) * P(!d|c) / P(!d) = = P(c) * (1 - P(d|c)) / (1 - P(d)) = = 0.5 * 0.3 / 0.4 = = 0.374(9) P(c|d) = P(c) * P(d|c) / P(d) = = 0.5 * 0.7 / 0.6 = 0.35 / 0.6 = = 0.58(3) ------------------------------- Bayes formula: P(a|b) = P(a) * P(b|a) / P(b) a resulting function: function takeMeasurement2() { const d = Math.random(1.0) < 0.6; const c = Math.random(1.0) < (d ? 0.58333 : 0.374999); return {c, d}; }
cratermoon 3y ago
Try this: full PDF: https://static1.squarespace.com/static/5fa2eabb04edcf45a65121de/t/64b41b0b3ee306752cc86ceb/1689525004116/MJ+and+risk+uni+and+bipolar.pdf https://static1.squarespace.com/static/5fa2eabb04edcf45a6512...
Vecr 3y ago
Obviously this is not a real argument against what you said, but the same thing happened with smoking. The tobacco companies said that there might be some set of factors that both caused lung disease, and caused smoking. As opposed to the alternate hypothesis that smoking caused lung disease. As is generally thought now, umbrellas do not cause rain but smoking does cause lung disease. Cannabis is federally illegal in the United States (the same way manufacturing machine guns without a license is for example), so if you live there don't use it. In some other places, I'd highly advise you to consider only the known benefits of using cannabis (and actually known, not something from a crappy study that you never read past the abstract or something a stoner buddy told you), and weigh that against known and potential risks of using the drug. Treat any form of smoking, including smoking cannabis, with high suspicion, as it involves inhaling large concentrations of combustion products. In conclusion, I would suggest you treat cannabis like how a rational person would treat cigarettes in the 1950s.
- sandofsky 3y ago
  > Obviously this is not a real argument against what you said, but the same thing happened with smoking. They showed the link between smoking and cancer with a mountain of empirical evidence, including tests on mice. https://www.mskcc.org/news/how-do-cigarettes-cause-cancer https://www.mskcc.org/news/how-do-cigarettes-cause-cancer > Cannabis is federally illegal in the United States Nobody cares anymore. > I'd highly advise you to consider only the known benefits of using cannabis Sugar has no health benefits and causes way more deaths. If you want to really change the world for the better, I suggest a war on ice cream. > Treat any form of smoking, including smoking cannabis, with high suspicion, as it involves inhaling large concentrations of combustion products. Finally, we agree: consume edibles.
  - Vecr 3y ago
    > They showed the link between smoking and cancer with a mountain of empirical evidence, including tests on mice. Yes, it does now. From what I've seen, people complain about how long it took for official action in labeling to take place on tobacco smoking, but it's up to them how much effort they want to put into preventing a repeat situation. For a somewhat related example, some people think the FDA puts so much effort into preventing a repeat of thalidomide that it's a net negative for the entire organization to exist at all, and suggest expanding the category of "legal but not (effectively) mandated" to include almost all proposed drugs. "Never again" is much harder and more costly than many people think, because you have to get it right (or err in the direction of preventing the type of outcome in question) every single time, but if a single thing gets through, it's all over. Make your decisions as rationally as possible. > I suggest a war on ice cream. Ice cream has some pretty funny statistics that I don't really buy as making actual sense. I'd limit usage quite heavily.
darkclouds 3y ago
That "whitey" effect people getting smoking weed and hash for the first few times is from a sudden release of glutathione, a powerful antioxidant stored in the liver, and a supplement Asian people have been known to take to whiten their skin.
avodonosov 3y ago
> people who develop depression and similar issues are likely to self-medicate with cannabis Note, the study speaks of risk of depression - depresion happens after they have cannabis use disorder. Hence "Subsequent" in the study title.

l33t7332273 3y ago

How is correlation vs causation determined in other studies where a control group cannot be used?

deleted 3y ago
[deleted]
PheonixPharts 3y ago
It's important to recognize that we've never successfully answered Hume's general skepticism about the existence of causality as something that is real outside of our own minds. So causality can't be positively confirmed to exist at all, let alone be detected and confirmed through statistics. Before you dismiss that is philosophical non-sense, causality is closely related to the "Arrow of Time" which is considered an unsolved problem in physics [0]. From what we've observed time appears to be the only asymmetric physical process, largely due to entropy (that is you can immediately tell if a video of a jar breaking is being played backwards because we don't expected a jar to "fall together"). There are Quantum processes, for example, that are time symmetric, that is playing a video of these processes would look the same reversed and forward. That said, as someone who does a lot of statistics, in practice what we do is model the causal process with a directed acyclic graph and see how well our models behave under causal assumptions. These work okay for answering practical questions about does A cause B. By controlling for correlating variables we can see the impact on what we believe to be a causal variable when we condition on these other correlating factors. Worth mention that nearly all of the work is done by linear models in practice. 0. https://en.wikipedia.org/wiki/Arrow_of_time https://en.wikipedia.org/wiki/Arrow_of_time
- xpe 3y ago
  The comment above refers to Hume's 'induction problem' [1]. > we've never successfully answered Hume's general skepticism about the existence of causality as something that is real outside of our own minds The quote seems to imply a strong consensus, but [1] disagrees. The phrasing "the existence of causality as something that is real outside of our own minds" sounds like philosophical idealism [2]. One can take that view without discussing causation at all, seems to me. Forgive the following blunt questions: how is philosophical idealism either (a) useful or (b) testable? To me, currently, it seems to be neither. Maybe I'm missing something? For those that get value from it, what are some practical take-aways you get out of thinking about Hume's 'problem of induction'? I'm very much into philosophy, but I admit I don't see a lot of utility in digging into this outside of historical purposes. It feels to me that the tools from inferential statistics combined with some deeper study on causation are far more useful and enlightening than trying to figure out the historical context around Hume's writing here. I'm glad other people do it, for sure, but what I've skimmed doesn't "grab" me. WRT causation, I often return to the idea of "no model is perfect, but some are useful". There are many times when we build useful models that -- when combined with proper experimentation -- very strongly indicate causation. The laws of physics, for example. As soon as people get involved, it gets messy. [1]: https://plato.stanford.edu/entries/induction-problem/ https://plato.stanford.edu/entries/induction-problem/ [2]: https://plato.stanford.edu/entries/idealism/ https://plato.stanford.edu/entries/idealism/
  - brutusborn 3y ago
    I completely agree that looking at old epistemology isn't that useful; simply because more recent theorists will need to address the same issues, so generally refer to older theory within their work. If you want a deep understanding then you need to look at the source material, but as you mention this is mostly only interesting for gaining a historical perspective. Unfortunately many of the people who create introductory science courses seem to mostly only be aware of Popper, so most students are aware of the importance of falsifiability but not of its problems. I suspect this is because reading Kuhn, Lakatos and Feyerabend leaves one with an impression that science isn't an objective endeavour and this is an uncomfortable truth when start from a belief that science is some infallible rationalist project. Personally it transformed my view from "trust the science"(TM) to "question the science, always." Science isn't always science unfortunately.
sacrosancty 3y ago
[dead]

labrador 3y ago

Don't bother trying to read the full text. They ask for all your information then you still can't read it.

nuclearnice3 3y ago

Indeed. Your reasoning was echoed in this critique from Prof David Curtis, Honorary Professor, UCL Genetics Institute. > “Around 20% of young people in Denmark use cannabis but in this study fewer than 1% of the sample had been assigned a diagnosis of cannabis use disorder and these may well have been people who had a pre-existing susceptibility to depression and other psychiatric disorders. > “Other studies have shown that cannabis use itself is not associated with increased risk of depression, so perhaps we are seeing that people with a pre-existing susceptibility to depression are more likely to be diagnosed with cannabis use disorder. This clearly does not mean that we should infer that cannabis somehow causes depression. > “I don’t think that this study provides us with much information to help decide the extent to which cannabis use is, or is not, harmful.” [1] https://www.sciencemediacentre.org/expert-reaction-to-study-of-cannabis-use-disorder-bipolar-disorder-and-depression/ https://www.sciencemediacentre.org/expert-reaction-to-study-...

taurath 3y ago

This precisely. Who’s going to utilize weed more? People going through mental anguish. You know what causes major depression anxiety and psychosis? Trauma. Read the ACE study. A cruel traumatizing environment and behaviors are far more to blame than cannabis.

avodonosov 3y ago

Isn't it possible to distinguish casuality between A and B from correlation by simply considering set sizes? !A & B A & !B A & B !A & !B A B !A !B

pseudocomposer 3y ago
Could you explain which distributions you think are correlation and which are causation?
- avodonosov 3y ago
  I hoped someone who knows statistics will just tell us, instead of me stretching my brain. The famous phrase "correlation does not mean casuation" is not equal to "correlation is indistingushable from casuation", but that's how people seem to often treat it. Ideally, the paper abstract should be clear about what can or can not be concluded. In the title they say "subsequent" as if suggesting casuality, but in the abstract they say "associated". Confusing for a layman such as me. My intuition of how to distinguish correlation from casuation: If cannabis and depression were symmetrically connected, then incease of chances for cannabis use between depressed was equal to the increase of chances for depression between cannabis users. If cannabis increases chances for depression more that depression increases chances for cannabis, than we conclude cannabis tends to cause depression. Formula: percentage of depressed between canabis users / percentage of depressed in general population > persentage of cannabis users between depressed / persentage of cannabis users in general population. Something like that.
  - avodonosov 3y ago
    He-he, the sides of inequality in my formula are always equal :) (c&d/c)/(d/total) > (c&d/d)/(c/total) c&d*total/c*d > c&d*total/d*c
    - avodonosov 3y ago
      Looking at it further, it's indeed not so easy. If we have a statistics produced by some casual relationship, e.g. function takeMeasurement() { // c (the cause) itself has probability 0.5 // d (the dependent) in absence of c has probability 0.5 // but the presence of c increases it to 0.7 const c = Math.random(1.0) < 0.5; const d = Math.random(1.0) < (c ? 0.7 : 0.5); return {c, d}; } one can calculate an equally plausible hypothesis of causality in opposite direction, which would produce an equal statistics: P(c) = 0.5 P(d|!c) = 0.5 P(d|c) = 0.7 P(d) = ? P(c|!d) = ? P(c|d) = ? ------------------------------- P(d) = P(c)*P(d|c) + P(!c)*P(d|!c) = = P(c)*P(d|c) + (1 - P(c))*P(d|!c) = = 0.5*0.7 + 0.5*0.5 = 0.35 + 0.25 = = 0.6 P(c|!d) = P(c) * P(!d|c) / P(!d) = = P(c) * (1 - P(d|c)) / (1 - P(d)) = = 0.5 * 0.3 / 0.4 = = 0.374(9) P(c|d) = P(c) * P(d|c) / P(d) = = 0.5 * 0.7 / 0.6 = 0.35 / 0.6 = = 0.58(3) ------------------------------- Bayes formula: P(a|b) = P(a) * P(b|a) / P(b) a resulting function: function takeMeasurement2() { const d = Math.random(1.0) < 0.6; const c = Math.random(1.0) < (d ? 0.58333 : 0.374999); return {c, d}; }

cratermoon 3y ago

Try this: full PDF: https://static1.squarespace.com/static/5fa2eabb04edcf45a65121de/t/64b41b0b3ee306752cc86ceb/1689525004116/MJ+and+risk+uni+and+bipolar.pdf https://static1.squarespace.com/static/5fa2eabb04edcf45a6512...

Vecr 3y ago

Obviously this is not a real argument against what you said, but the same thing happened with smoking. The tobacco companies said that there might be some set of factors that both caused lung disease, and caused smoking. As opposed to the alternate hypothesis that smoking caused lung disease. As is generally thought now, umbrellas do not cause rain but smoking does cause lung disease. Cannabis is federally illegal in the United States (the same way manufacturing machine guns without a license is for example), so if you live there don't use it. In some other places, I'd highly advise you to consider only the known benefits of using cannabis (and actually known, not something from a crappy study that you never read past the abstract or something a stoner buddy told you), and weigh that against known and potential risks of using the drug. Treat any form of smoking, including smoking cannabis, with high suspicion, as it involves inhaling large concentrations of combustion products. In conclusion, I would suggest you treat cannabis like how a rational person would treat cigarettes in the 1950s.

sandofsky 3y ago
> Obviously this is not a real argument against what you said, but the same thing happened with smoking. They showed the link between smoking and cancer with a mountain of empirical evidence, including tests on mice. https://www.mskcc.org/news/how-do-cigarettes-cause-cancer https://www.mskcc.org/news/how-do-cigarettes-cause-cancer > Cannabis is federally illegal in the United States Nobody cares anymore. > I'd highly advise you to consider only the known benefits of using cannabis Sugar has no health benefits and causes way more deaths. If you want to really change the world for the better, I suggest a war on ice cream. > Treat any form of smoking, including smoking cannabis, with high suspicion, as it involves inhaling large concentrations of combustion products. Finally, we agree: consume edibles.
- Vecr 3y ago
  > They showed the link between smoking and cancer with a mountain of empirical evidence, including tests on mice. Yes, it does now. From what I've seen, people complain about how long it took for official action in labeling to take place on tobacco smoking, but it's up to them how much effort they want to put into preventing a repeat situation. For a somewhat related example, some people think the FDA puts so much effort into preventing a repeat of thalidomide that it's a net negative for the entire organization to exist at all, and suggest expanding the category of "legal but not (effectively) mandated" to include almost all proposed drugs. "Never again" is much harder and more costly than many people think, because you have to get it right (or err in the direction of preventing the type of outcome in question) every single time, but if a single thing gets through, it's all over. Make your decisions as rationally as possible. > I suggest a war on ice cream. Ice cream has some pretty funny statistics that I don't really buy as making actual sense. I'd limit usage quite heavily.

darkclouds 3y ago

That "whitey" effect people getting smoking weed and hash for the first few times is from a sudden release of glutathione, a powerful antioxidant stored in the liver, and a supplement Asian people have been known to take to whiten their skin.

avodonosov 3y ago

> people who develop depression and similar issues are likely to self-medicate with cannabis Note, the study speaks of risk of depression - depresion happens after they have cannabis use disorder. Hence "Subsequent" in the study title.