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Feyerabend's counterinduction and Bayesianism. Has anyone here thought about how these two views of science bear on each other?
In response to
Open Thread April 8 - April 14 2014
Belief & double-blind randomized control group studies: response to IlyaShpitser
In a previous thread IlyaShpitser said >According to your blog, you don't believe in RCTs, right? What do you believe in?
This is part of the problem I'm trying to address. Belief/non-belief are inappropriate locutions to use in terms not only of the double-blind randomized control group method (DBRCGM), but of models and methods of science in general. "Belief in" a any scientific method is not even remotely relevant to science or the philosophy of science. Also, I did not say that the DBRCGM is entirely useless. All I'm really saying is it can be improved upon. Furthermore, what I "believe in" is almost entirely irrelevant to my appreciation of Bayesiansim and other forms of scientific fallibilistic flexibility. When we "believe in" something, we allay our curiosity and create unnecessary obstacles for the mind changes Bayesianism and fallibilistic flexibility encourage us to practice.
Smoking is not "accused" of being strongly correlated with negative outcomes. It is strongly correlated with negative outcomes, as a simple empirical fact. This is a statement about the joint distribution of the observed variables "smoking" and "negative outcomes", and it has nothing to do with causal inference. I cannot even imagine a scenario where the statement "Smoking is strongly correlated with lung cancer" is false, short of a vast conspiracy among scientists and doctors
A slightly more interesting question is whether the correlation between smoking and cancer is due to causation. It is theoretically possible that an unmeasured confounder is responsible for the observed correlation. In fact, R.A. Fisher believed such a confounder was probably at work . One of the first uses of sensitivity analysis was to show how unrealistic Fisher's claim was. A sensitivity analysis is essentially a thought experiment that lets you play around with how "strong" a confounder has to be, in order to account for the observed correlation if the causal null hypothesis were true. See http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755131/.
In this case, I think any reasonable investigator who looks at the data and does some basic reasoning about possible confounders, will come away with a very strong posterior in favor of smoking causing lung cancer. However, the relationship between smoking and certain other negative outcomes is in some cases much more questionable, and it would not surprise me if publication bias accounts for many of the negative outcomes smoking has been connected to
Anders_H: "Smoking is not "accused" of being strongly correlated with negative outcomes. It is strongly correlated with negative outcomes...."
This is the opposite conclusion of the first citation I provided. And the second "in house" LW link asserts that in terms of decision making about smoking in light of whether or not it's linked to cancer is about a 50/50 proposition.
Anders_H: "...as a simple empirical fact." This is a huge abstraction. Please clarify.
Anders_H: "This is a statement about the joint distribution of the observed variables "smoking" and "negative outcomes", and it has nothing to do with causal inference."
I understand that, but I'm not asking about that. I'm asking why the correlations are thought of as causes by reports on the relationship. And it is indeed an ACCUSATION commonly presented by the press, etc..., that smoking causes or is positively correlated to cancer. Furthermore, ccording to Hume, causal inferences are THEMSELVES observed by constant conjunction, implying we have know sure way of knowing what the relationship between causes and correlations is.
Anders_H: "I cannot even imagine a scenario where the statement "Smoking is strongly correlated with lung cancer" is false...."
Again, I refer you to the first citation, which also underscores the fact the line between "weak" and "strong" is done by fiat, another challenge to the so-called link between smoking and cancer.
The Japanese smoke more (if not the most) than most cultures yet are also one of the most healthy cultures. This goes to your "slightly more interesting question," but it also goes the challenges of positively correlating smoking with "negative" outcomes. A further problem is that "negative outcomes" are normatively tied to cultural standards. Another problem is with average life expectancy comparisons, as they are to sensitive to outlier inflation.
WHY IS IT SO OFTEN REPEATED THAT SMOKING CAUSES CANCER?
Start here. Follow the references (and the references' references). If you are still not convinced then try here.
Did you follow the references I provided? Two of them are LW "in house" and the rest are superior to the ones you cited.
Do you believe the nutritional etiology of scurvy?
Yes.
In response to
Open Thread April 8 - April 14 2014
Continuing Causality Woes: Smoking and Lung Cancer:
Looking at:
http://lesswrong.com/lw/cc8/seq_rerun_changing_the_definition_of_science/
and
http://wiki.lesswrong.com/wiki/Smoking_lesion
Cross Referenced with Causation in the Presence of Weak Associations: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024843/
WHY IS IT SO OFTEN REPEATED THAT SMOKING CAUSES CANCER? I'm not a tobacco user, so I'm not trying to justify my behavior. Has anyone here looked into the other things tobacco's accused of causing or being "strongly" correlated with?
Background reading:
-Anything by David Hume
-Carl G. Hempel. Laws and Their Role in Scientific Explanation: http://www.scribd.com/doc/19536968/Carl-G-Hempel-Laws-and-Their-Role-in-Scientific-Explanation
-Studies in the Logic of Explanation: http://www.sfu.ca/~jillmc/Hempel%20and%20Oppenheim.pdf
-Causation as Folk Science: http://www.pitt.edu/~jdnorton/papers/003004.pdf
-Causation: The elusive grail of epidemiology: http://link.springer.com/article/10.1023%2FA%3A1009970730507
-Causality and the Interpretation of Epidemiologic Evidence: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513293/
-Studies in the Philosophy of Biology: Reduction and Related Problems: http://books.google.com/books?id=NMAf65cDmAQC&pg=PA3#v=onepage&q&f=false
In response to
[SEQ RERUN] Changing the Definition of Science
This is a good one. I definitely sympathize with Eliezer's point that Bayesian probability theory is only part of the solution. e.g., in philosophy of science, the deductive-nomological account of scientific explanation is being displaced by a mechanistic view of explanation. In this context, a mechanism is an organization of parts which is responsible for some phenomena. This change is driven by the inapplicability of D-N to certain areas of science, especially the biomedical sciences, where matters are more complex and we can't really deduce conclusions from universal laws; instead, people are treating law-like regularity as phenomena to be explained by appeal to the organized interactions of underlying parts.
e.g., Instead of explaining, "You display symptoms Y; All people with symptoms Y have disease X; Therefore, you have disease X," mechanists explain by positing a mechanism, the functioning of which constitutes the phenomena to be explained. This seems to me to be intimately related to Eliezer's "reduce-to-algorithm" stance; and that an appeal to reduce abstract beliefs to physical mechanisms seems to be a pretty good way to generalize his stance here. In addition, certain mechanistic philosophers have done work to connect mechanisms and mechanistic explanation with Bayesian probability, and with Pearl's work on Bayesian networks and causality. Jon Williamson at Kent has my favorite account: he uses Recursive Bayesian Networks to model this sort of mechanistic thinking quantitatively.
Relevant: -Anything by David Hume -Carl G. Hempel. Laws and Their Role in Scientific Explanation: http://www.scribd.com/doc/19536968/Carl-G-Hempel-Laws-and-Their-Role-in-Scientific-Explanation -Studies in the Logic of Explanation: http://www.sfu.ca/~jillmc/Hempel%20and%20Oppenheim.pdf -Causation as Folk Science: http://www.pitt.edu/~jdnorton/papers/003004.pdf -Causation: The elusive grail of epidemiology: http://link.springer.com/article/10.1023%2FA%3A1009970730507 -Causality and the Interpretation of Epidemiologic Evidence: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513293/ -Studies in the Philosophy of Biology: Reduction and Related Problems: http://books.google.com/books?id=NMAf65cDmAQC&pg=PA3#v=onepage&q&f=false
If you look at the wikipedia page that describes fallibilism, the word probability doesn't directly appear. In the main body of the article.
People like Pyrrho were practicing fallibilism long before the kind of math that you need to think about probabilities that you can multiple with each other got invented.
So the underlying philosophies are extremely similar if not the same even though the methods, largely due to practical problems (lack or presence of mathematical tools)?
In response to
Open Thread April 8 - April 14 2014
What are the differences and similarities between fallibilism and Bayesianism?
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I don't understand why you're getting downvoted. Those were great links, and indeed relevant. I appreciated them.
Thanks. I don't know either. That's why I don't come here that often. The karma points system doesn't serve the aims of science. It serves the "scientific consensus" myth which is mostly a glorified popularity contest without regard for fallibilism, iteration, paradigm shifting and counterinduction.