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Does X cause Y? An in-depth evidence review (2021)

232 点作者 l0b03 个月前

32 条评论

levocardia3 个月前
Seems very dismissive and unaware of recent advances in causal inference (cf other comments on Pearl). Putting &quot;throw the kitchen sink at it&quot; regression a la early 2000s nutritional research (which is indeed garbage in garbage out) in the same category as mendelian randomization, DAGs, IP weighting, and G-methods is misleading. I do worry that some of these EA types dive head-first into a random smattering of google scholar searches with no subject matter expertise, find a mess of studies, then conclude &quot;ah well, better just trust my super rational bayesian priors!&quot; instead of talking with a current subject matter expert. Research -- even observational research -- has changed a lot since the days of &quot;one-week observational study on a few dozen preschoolers.&quot;<p>A more general observation: If your conclusion after reading a bunch of studies is &quot;wow I really don&#x27;t understand the fancy math they&#x27;re doing here&quot; then <i>usually</i> you should do the work to understand that math before you conclude that it&#x27;s all a load of crap. Not always, of course, but usually.
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mnky9800n3 个月前
In my own research we are investigating how fluids cause changes in rocks that allow for mineralization of CO2 and have such problems of confounding variables (not terribly unique I suppose). One thing we note is that, well, fluid comes from the sky and goes into the ground. Thus, the deeper you go, the less fluid there is since the pathways from the sky to deep into the ground become more sparse as well as needing higher pressures to enter these regions to either overcome capillary pressures in existing fracture zones or to literally break the rock (which is highly unlikely using naturally occuring pressures from fluids from the sky). And so, literally everything in all the data sets correlates with depth in some way. But in what way? well this has many dependencies as well, did the rock that absorbed some of the fluids grow in volume because of a chemical change? are the fluid pathways currently connected? What kind of rock is absorbing the fluids? Are microbes in the fluid absorbing contents from the fluid that would otherwise be used for rock changes? and so you are left with this giant pile of data (tens of terabytes) without a clear connection between fluid and rock interactions except that there is less fluids from the sky the deeper you go into the rock. This is obvious, however it is also rather unhelpful when trying to understand the other processes that exist. Of course you might say, have you tried detrending your data? And the answer is yes and to no effect. The simple truth is that this depth dependency interacts in different ways with different systems and there is no easy way to figure out how it does for each sub-system such as the fluid rock chemistry interactions, the rock fracture mechanics, the subsequent methane and hydrogen that is produced and likely consumed by microbes, etc.
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Kaotique3 个月前
I think a lot of these kinds of studies are not really about objectively studying a phenomenon but trying to prove a predetermined point. The study is designed and adjusted until it proves what it should prove. Then it&#x27;s wrapped in a nice news headline which goes away with all the details and subtleties and used for political or economic gain. Reproducing the results is not interesting and not funded. Other studies are then using these results as sources to stack the house of cards even higher. I think this does a lot of harm to science as a whole because a lot of people disregard all scientific results as a result.
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uniqueuid3 个月前
Oh what fun to discover the horror of causality!<p>For some areas of research, truly understanding causality is essentially impossible - if well-controlled experiments are impossible and the list of possible colliders and confounders is unknowable.<p>The key problem is that <i>any</i> causal relation can be an illusion caused by some other, unobserved relation!<p>This means that in order to show fully valid causal effect estimates, we need to<p>- measure precisely<p>- measure all relevant variables<p>- actively NOT measure all harmful (i.e. falsely correlated) variables<p>I heartily recommend the book of why [1] by Pearl and Mackenzie for a deeper reading and the &quot;haunted DAG&quot; in McElreath&#x27;s wonderful Statistical Rethinking.<p>[1] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;The_Book_of_Why" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;The_Book_of_Why</a>
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laurentlb3 个月前
On a similar note, I enjoyed watching the video: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;mQ56uOkjccg?si=1hpwGqv2dQqLQ-ME" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;mQ56uOkjccg?si=1hpwGqv2dQqLQ-ME</a> (by Nutrition Made Simple!)<p>It takes a specific topic (here, health effects of red meat) and explains how each type of study can provide information, without proving anything. It helped me a lot understand the science related to nutrition, where you never have perfect studies.
KempyKolibri3 个月前
Dismissing all observational study designs out of hand because they can be difficult and easy to perform poorly seems like quite the take.<p>I see this all the time in people’s interpretation of nutrition research, and they do exactly as this article suggests and fall back to the “intuitive option”, and go onto some woo diet that they eventually give up because they start feeling awful.<p>I would disagree that observational study designs should be thrown out the window or that it makes sense to, as this article seems to do, lump cross-sectional ecological data in with prospective cohort studies.<p>Things often “make intuitive sense” only because of these study designs. We used to get kids to smoke pipes to stave off chest infections because it made “intuitive sense” and it’s only because of observational studies that we now believe smoking causes lung cancer.<p>The direction of evidence from prospective cohort studies to RCTs in the field of nutrition science on intake vs intake shows a 92% agreement. If we take RCTs to be the “gold standard” of evidence that best tracks with reality, it seems a little odd that these deeply flawed observational studies that we should apparently disregard seem to do such a good job coming to the correct conclusions.<p><a href="https:&#x2F;&#x2F;bmcmedicine.biomedcentral.com&#x2F;articles&#x2F;10.1186&#x2F;s12916-025-03860-2" rel="nofollow">https:&#x2F;&#x2F;bmcmedicine.biomedcentral.com&#x2F;articles&#x2F;10.1186&#x2F;s1291...</a>
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talkingtab3 个月前
I am slowly becoming convinced that studies are in fact cargo-cultism. And there are many, many studies that confirm this.<p>But about causality. Long ago (old cars) I had a friend who told me that most mornings his car would not start until he opened the hood and wrapped some wires with tape (off with the old tape on with the new). Then the car would start. Every now and then it would take two wraps. Hmmm.<p>After he demonstrated this, I decided to try to help. I followed the wires that were wrapped. Two of them. To my surprise they were not connected at either end. This was insane, and yet his study - and my own observation - demonstrated that wrapping these two wires which were completely disconnected caused his car to start. Now there is causality for you.<p>Except that if you have a more complex model of cars, there is a sane explanation. Again this is an old car with a carburetor. In case you don&#x27;t know this is a little bowl of gas it that provides a combustible mix of air and gas. If there is too much gas then your car won&#x27;t work. The mix is controlled by a little float that controls the level of gas in the little bowl. Toilet bowls work on the same principle.<p>If your float is bad (or other issues) your car engine would get too much gas - be &quot;flooded&quot; and you have to wait until much of it evaporates. So if you flood your car engine, go and wrap some wires, it may be that your car will start right up.<p>So I rebuilt the carburetor and my friend never had that problem again.<p>The moral of the story is that I had better &quot;model&quot; of how cars work. But in the back of my mind I am aware that my model may be or have been just as deficient. Did you know that we are bombarded from space by an unknown type of neutrino that stops electricity from working unless there is a little pool of some liquid nearby or it is Thursday. I am going to do a study of this.<p>There are very good reasons to understand how frail our ability to understand causality is. And we are talking simple things here. The scientific method is about EXPERIMENTS. Yes, I did that in bold. Doing things. We have deeply complex situations we need to understand and in my opinion, studies do not help.
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thenoblesunfish3 个月前
As with many things, just understand what you are trying to do.<p>If you want to <i>predict</i> Y and you know X, you can use data that tell you when they happen together.<p>If you are trying to <i>cause</i> (or prevent) Y, it&#x27;s harder. If you can&#x27;t do experiments (e.g. macroeconomics), it&#x27;s borderline impossible.
groby_b3 个月前
&quot;a technique called regression analysis that, as far as I can determine, cannot be explained in a simple, intuitive way (especially not in terms of how it &quot;controls for&quot; confounders)&quot;<p>That sounds very much like a skills issue. Because it can. You call out what you consider might be confounders as independent variables (covariates). You can then use regression analysis to estimate the individual contributions from each confounder, and control for them by essentially filtering out that contribution.<p>Is reality harder than that? Yes. Much. The world of science isn&#x27;t 9th grade math, sorry. You are not entitled to understand everything deeply with 5 minutes of mediocre effort.
BugsJustFindMe3 个月前
&gt; <i>Now, a lot of these studies try to &quot;control for&quot; the problem I just stated - they say things like &quot;We examined the effect of X and Y, while controlling for Z [e.g., how wealthy or educated the people&#x2F;countries&#x2F;whatever are].&quot; How do they do this? The short answer is, well, hm, jeez.</i><p>You mean they don&#x27;t cluster the data into sets of overlapping bins where the controlled attribute has approximately the same value and then look for the presence of an XY relationship within the bins instead of across them?
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gns243 个月前
&quot;A study using a complex mathematical technique claiming to cleanly isolate the effect of X and Y. I can&#x27;t really follow what it&#x27;s doing...&quot;<p>This is a frustrating type of issue. Dismissing something with &quot;I don&#x27;t understand this, but I don&#x27;t believe it&quot; isn&#x27;t the sort of thing I want to be doing. However, I don&#x27;t have any desire to waste time trying to understand what someone has done (and did they really understand what they were doing themselves?) when it&#x27;s clear that the effect isn&#x27;t cleanly isolated in the data and no amount of mathematics is going to change that.
sujumayas3 个月前
Am I the only one thinking through the reading of this: &quot;Wait a minute... isn&#x27;t this article some kind of weak X then Y also? Observation of many cases, with generalized causality concludes that he just feels like x should cause Y? hahaha. Love the article btw.
spacebanana73 个月前
I disagree strongly with this mathematised notion of causality. Two things can be perfectly correlated at all observed points in history without necessarily being causal. There can always be some unknown variable driving change in both.
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ngriffiths3 个月前
And all this before you even get to &quot;how much of an impact of X on Y should there be before it is even close to a bottleneck that&#x27;s worth actually acting on, and do we think it reaches <i>that</i> threshold?&quot;
daoboy3 个月前
It&#x27;s layers of abstraction all the way down the light cone.<p>The causality is always present, we just don&#x27;t have the processing power to ensure with 100% certainty that all relevant factors are accounted for and all spurious factors dismissed.
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einpoklum3 个月前
&gt; <i>I have to say, this all was simultaneously more fascinating and less informative than I expected it would be going in.</i><p>Direct quote from the author of this post and I couldn&#x27;t agree more, particulartly about the post itself.
dang3 个月前
Related. Others?<p><i>Does X cause Y? An in-depth evidence review</i> - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30613882">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30613882</a> - March 2022 (3 comments)
tomrod3 个月前
Previous post: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42965989">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42965989</a>
jtrn3 个月前
As a clinical psychologist, I find it increasingly frustrating to sift through research studies that fail to meet even the most basic standards of scientific rigor. The sheer volume of studies that claim “X is linked to Y” without properly addressing the correlation-versus-causation fallacy is staggering. It’s not just an oversight—it’s a fundamental flaw that undermines the credibility and utility of psychological research.<p>If a study is publicly funded, there should be a minimum requirement: it must include at least two research arms—one with an experimentally manipulated variable and a proper control condition. Furthermore, no study should be considered conclusive until its findings have been successfully replicated, demonstrating a consistent predictive effect. This isn’t an unreasonable demand; it’s the foundation of real science. Yet, in clinical psychology, spineless researchers and overly cautious annd&#x2F;or power crazed ethics committees have effectively neutered most studies into passive, observational, and ultimately useless exercises in statistical storytelling.<p>And for the love of all that is scientific, we need to stop the obsession with p-values. Statistical significance is meaningless if it doesn’t translate into real-world impact. Instead of reporting p-values as if they prove anything on their own, researchers should prioritize effect sizes that demonstrate meaningful clinical relevance. Otherwise, we’re left with a field drowning in “statistically significant” noise—impressive on paper but useless in practice.
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fritzo3 个月前
I like this writing style with unbound variables. Reminds me of Maya Binyam&#x27;s novel &quot;Hangman&quot;, or Kafka&#x27;s novels.
m3kw93 个月前
so if we have a scenario where we have data points where when X ball moves white ball also moves, but we’re missing some direct evidence where they actually hit each other or not. But they correlate from the limited sample. I think this is what most correlations are like, we do not see the direct atoms causing the causation, only a probability
zkmon3 个月前
There is no causality, what so ever. The perceived causality is built backwards, only to make something appear sensible. Every event in this universe contributes as a cause to every other event in the universe. It&#x27;s like fluid flow. Every molecule of the fluid affects the movement of every other molecule. The world evolves in a fluid motion, not through isolated causal chains.
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skirge3 个月前
Most important factor on results of research are personal beliefs, especially in &quot;economics&quot;.
Cappor3 个月前
The question of whether X can cause Y remains open and requires further research. The article highlights the importance of thoroughly checking sources and methodology to draw clear conclusions. This is an important step towards a deeper understanding of such relationships.
epidemiology3 个月前
In introductory epidemiology courses you&#x27;ll usually get the Bradford Hill criteria in the first week or two, which gives a good foundation of determining public health causality. After digging deeper, the entire field of causal inference is revealed.<p>A healthy respect for the difficulties of determining causality is beneficial. Irrational skepticism ignoring the evidence of strong observational research simply replaces it with... what exactly? That&#x27;s how we ended up with an 71 year old anti-vaccine conspiracist as the health secretary.
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Chance-Device3 个月前
Well, of course the conclusion is that you don’t know, Mr. Author. Because the very thing that triggered your interest in the subject of X and Y was that there was no clear cut consensus on the subject. If there were, you wouldn’t have needed to do research at any level of depth at all, because those findings would be well known, and you’d have found them easily through a simple web search.<p>Instead you were drawn to a topic which seemed ambiguous, which had multiple possible interpretations, multiple plausible angles, and on which nobody could agree. You didn’t explicitly know these things starting out, but they were embedded in the very circumstances which caused you to investigate the subject further.<p>Yes, determining causation is sometimes hard, is it also sometimes very easy. However, very easy answers are not interesting ones, and so we find ourselves here.
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msarrel3 个月前
The variable you really have to worry about is z.
Temporary_313373 个月前
And don’t even get me started on A leading to B
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skyde3 个月前
is it only me or this completely miss all the recent research on causal inference using causal graphical model ?
aqueueaqueue3 个月前
So, Bayesian or Frequentist?
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stickfigure3 个月前
I can&#x27;t believe nobody has posted the obvious XKCD of relevance yet:<p><a href="https:&#x2F;&#x2F;xkcd.com&#x2F;552&#x2F;" rel="nofollow">https:&#x2F;&#x2F;xkcd.com&#x2F;552&#x2F;</a>
daft_pink3 个月前
After reading this article, it would be really interesting to have a study on whether they can do research to indicate when correlation == causation and when correlation != causation for any given study and what the factors and a tool so we can have a simple risk assessment on whether there is a link or not.