A. Correlation vs Causation (4)
B. (4)
C. (5) GRE and Philosophy
LSAT and Philosophy
D.
E. Randomized trials of aid to third world countries: Free textbooks vs Subsidized meals vs Free uniforms vs Treat intestinal worms.
http://www.wired.com/wiredscience/2013/11/jpal-randomized-trials/
(You may use this article for a blog review)
http://www.hsph.harvard.edu/hicrc/firearms-research/guns-and-death/
G. Sweden, Dutch, and US studies of Autism and Thimerisol
http://www.ncbi.nlm.nih.gov/pubmed/24083600/
http://www.cdc.gov/sids/
H. (2)
I. Literature and empathy
https://www.google.com/search?q=study+shows+effects+of+reading+classical+literature+&oq=study+shows+effects+of+reading+classical+literature+&aqs=chrome..69i57.27192j0j4&sourceid=chrome&espv=210&es_sm=91&ie=UTF-8
The actual study: http://www.sciencemag.org/content/342/6156/377.abstract
J. MMR vaccines
To know if vaccines cause autism, what would you need to know?
What about risk of anaphylaxis?
Post-Wakefeild study measles case in UK: http://news.bbc.co.uk/2/hi/uk_news/england/5081286.stm
In the US: http://theness.com/neurologicablog/index.php/measles-outbreak-thanks-jenny/
K. Knee surgery NYT
Knee surgery WSJ
L.
N. Novella on Acupunture
O. I don't drink tequila anymore. Every time I drink it, I end passed-out in some stranger's hotel room. I prefer to stick to other types of alcohol.
P. EMF Studies
Natural News EMF
EMF Study
Q. Is there something about ice cream that causes me to like it or is there something about me that causes me to like ice cream?
R. The Cancer Cluster Myth
S. Rising Autism Rates:
"According to the Centers for Disease Control the number of autism cases among 8-year-olds increased 57 percent from 2002 to the 2006. Looking back over the last 20 years, the rates of autism have gone up 200 percent. Today, 1 in 70 male children has some form of autism spectrum disorder."
T. Juicing/Diet X Caused My Weight-loss.
U. Belief in Moral Realism and Moral Action (5)
V. Euthyphro
W.
X.EMF radiation causes poor plant growth
Y. Direction of Causation:
Our results might not apply to people of other ethnic origins, such as those with a high prevalence of lactose intolerance, or to children and adolescents. Nutrient concentrations in milk and other dairy products are variable and depend on factors such as food fortification, biosynthesis, the animal’s diet, and physicochemical conditions,51which might affect the generalisability of our results. Theoretically, the findings on fractures might be explained by a reverse causation phenomenon, where people with a higher predisposition for osteoporosis may have deliberately increased their milk intake. We investigated time to first fracture, which reduces the likelihood of biased estimates. Furthermore, high milk consumption was also related to higher mortality among those without a fracture during follow-up. In the analyses we did not consider fractures caused by metastatic cancer, but cases of fractures due to suspected high impact trauma were, as recommended,36 37 retained in the analysis since these fractures are—as ordinary fragility fractures—also more common in those with low bone mineral density. The possibility of a reverse causation theory is also contradicted by the fact that fermented dairy products were related to a reduced risk of fracture and that a personal or a family history of hip fracture was not associated with a higher milk intake. Additionally, the change in average reported consumption of milk in the Swedish Mammography Cohort during a long follow-up was not affected by change in comorbidity status. Furthermore, prospective designs are more likely to generate non-differential misclassification and thus attenuate the evaluated association. None the less, we cannot rule out the possibility that our design or analysis failed to capture a reverse causation phenomenon. http://www.bmj.com/content/349/bmj.g6015
Y. Many things that correlate with rise in autism diagnosis
Z. Just interpreting effects: aspartame and gut bacteria
Definitions and Terminology:
Causal Argument: An inductive argument whose conclusion contains a causal claim.
Implied Structure of a General Causal Argument
(P1) X is correlated with Y.
(P2)* The correlation between X and Y is not due to chance (i.e., it is not merely statistical or temporal).
(P3) The correlation between X and Y is not due to some mutual cause Z or some other cause.
(P4) Y is not the cause of X. (Direction of causation).
(C): X causes Y.
*In (P2), to show that the correlation isn't merely due to chance there should be a proposed causal mechanism.
Example Argument: The MMR vaccine (X) causes a decrease in measles incidence rates (Y).
(P1) Taking the MMR vaccine is correlated with a lower rate of measles incidence. When vaccination rates go up in a population, incidence rates go down. When vaccination rates go down in a population, incidence rates go up.
(P2) The correlation between taking the vaccine and lower rates of incidence is not due to chance. Proposed causal mechanism: Infectious diseases are spread via micro-organisms. Vaccines cause the immune system to produce antigens that bring about resistance to contact with the associated micro-organism.
(P3) The correlation between vaccines and incidence rates are not due to some mutual cause. For example, greater sanitation, nutrition, and hygiene doesn't explain all the changes in incidence rates pre and post vaccine since vaccines we introduced at different times but the other variables were all introduced at the same time.
(P4) Lower incidence rates don't cause people to get vaccines in greater numbers.
(C) The MMR vaccine causes lower incidence rates for measles.
All of the same concepts that apply to generalizations and polling also apply to causal arguments:
Sample size (hasty generalization), sample bias, selection bias, measurement error, vagueness & ambiguity, reporting in the media vs actual study findings.
Common Fallacies and Reasoning Errors Associated with Causal Reasoning
1. Post hoc ergo proptor hoc (after therefore because of). This is known as confusing causation with temporal order. Just because Y happened after X it doesn't follow that X caused Y. For example, everyday I eat peanut butter and toast for breakfast then go to work. It doesn't follow that eating peanut butter and toast causes me to go to work. This error applies to (P1), (P2), and (P3).
2. Misidentifying the Relevant Causal Factor(s): For any given general causal relationship there are often hundreds of factors common to each causal event. It does not follow that they are all relevant. This is why it's important to hypothesis a (possible) causal mechanism. For example, suppose you go out to dinner with 8 friends, 3 of which got sick a few hours after eating. It turns out that the 3 friends are all male. If you were to conclude that they got sick because they are all male this would be to misidentify the relevant causal factor. It seems unlikely that there is a causal relationship between their gender and their illness. This common variable is irrelevant. More likely their illness has to do with what they ate or drank in common. This reasoning error is usually a consequence of not having very deep knowledge of the topic at hand. A little wikipedia research can usually at least get you started in the right direction. This error applies to (P1), (P2), and (P3).
3. Mishandling Multiple Factors: As with identifying relevant causal factors, for every general causal argument there will often be many antecedent variables involved. Identifying the one that has causal import can be tricky. Again, as in above, you want to find ways to falsify competing alternatives. Also, people will often fail to consider alternative causal variables to the one(s) they are identify. Again, a little wiki-reseach gets you started. This error applies to (P1), (P2), and (P3).
4. Confusing Correlation and Causation: Just because two events or variables are correlated, it doesn't follow necessarily that there's a causal relationship. For example, just because there's a correlation between sales of organic foods and autism rates, it doesn't follow that there's a causal relationship between the two. Often a good way to avoid committing this error is to see if you can come up with a likely causal mechanism. If you can't then it's likely simply correlation. However, you could be wrong, so do a little digging online just in case. This error applies to (P2) and (P3).
5. Confusing Cause and Effect (aka Direction of Causation). Often it is difficult to disentangle the direction of causation. For example, does participation in high school sports cause the development of a good work ethic and perseverance or do people with a good work ethic and perseverance have a greater tendency to do sports? This error applies to (P4).
6. No Control (see Mill's Methods: Method of Difference). Often misattributions of causation occur because there is no control group. If we don't know the natural prevalence rate of a disease or its average natural healing time we cannot reasonably attribute causal power to a purported remedy. The same goes for social policy interventions. Being able to compare an intervention group to a non-intervention group improves our ability to attribute (or dismiss) causation to the intervention. Applying a control helps eliminate errors in (P1), (P2), (P3), and (P4).
Mills Methods
Method of Agreement: If two or more occurrences of a phenomena have only one relevant factor in common, that factor must be the cause.
Example:
Case 1: Factors a, b, and c are followed by E (effect).
Case 2: Factors a, c, and d are followed by E.
Case 3: Factors b and c are followed by E.
Case 4: Factors c and d are followed by E.
Therefore, factor c is probably the cause of E.
Method of Difference: The probable cause (C) of an event/effect (E) is present when E occurs and C is absent when E doesn't occur.
Case 1: Factors a, b, and c are followed by E.
Case 2: Factors a and b are not followed by E.
Therefore, factor c is probably the cause of E.
Homework 11B
Part 1: P. 246 Ex. 9C (b), (c), (d), (e) Instructions: You don't need to diagram the arguments. You are only required to put (c) and (d) into their formal structure; however, you should analyze all the arguments in terms of the concepts we have discussed in class.
Part 2: Mini Research Project: Is smoking marijuana causally relevant to whether someone will try harder drugs? (I.e., Is marijuana a gateway drug?) Things to look at: What are the other variables that are shared by hard-drug users. Which ones are statistically significant?
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