Lecture 13B: Pseudoscience and Scientific Reasoning Part 1

Business:

1)  Second chance at excellence is due as a hard copy in class on Monday.  Please include your name and NSHE.

Powerband/Applied Kinesiology and Testing

Pseudoscience, Spotting it, and How to Avoid Falling for It

James Randy and psychic surgery

Explaining Why Many People Don't Believe Belief the Scientific Consensus
http://www.nytimes.com/2014/07/06/upshot/when-beliefs-and-facts-collide.html?smid=fb-nytimes&WT.z_sma=UP_WBA_20140707&bicmp=AD&bicmlukp=WT.mc_id&bicmst=1388552400000&bicmet=1420088400000&_r=2

http://www.nytimes.com/2014/05/09/upshot/vaccine-opponents-can-be-immune-to-education.html?action=click&contentCollection=The+Upshot&module=RelatedCoverage&region=Marginalia&pgtype=article&_r=0

Self-Deception and Testing

John Oliver on Science

Self-Deception in clinical trials

Therapeutic Touch, Non-Falsifiability, and Motivated Reasoning

Dousing and the Ideomotor Effect

California Farmers

Rom Houben
Ideomotor effect bomb detectors and liver disease detectors.


Nocebo and Placebo

EMF Sensitivity and nocebo:
a) Powerlines and causal explanations
b) Cell phone towers and wireless internet

Gluten and Nocebo

Self-Deception and Clinical Trials 

Placebo Effect:  A placebo is an inert substance that creates either a positive response or a negative response in a patient who takes it. The phenomenon in which a placebo creates a positive response in the patient to which it is administered is called the placebo effect.

“We found little evidence in general that placebos had powerful clinical effects. Although placebos had no significant effects on objective or binary outcomes, they had possible small benefits in studies with continuous subjective outcomes and for the treatment of pain. Outside the setting of clinical trials, there is no justification for the use of placebos.”

[IS THE PLACEBO POWERLESS? An randomized of Clinical Trials Comparing Placebo with No Treatment N Engl J Med, Vol. 344, No. 21 • May 24, 2001]

Nocebo Effect:  In medicine, a nocebo (Latin for "I shall harm") is a harmless substance that creates harmful effects in a patient who takes it. The nocebo effect is the negative reaction experienced by a patient who receives a nocebo. (Wikipedia)

What should we do about placebo and nocebo in constructing trials?

Memory and Confirmation Bias: https://aeon.co/ideas/bad-thoughts-can-t-make-you-sick-that-s-just-magical-thinking


Fluoridation Studies/Article

Looking for citations: natural news positive thoughts and healing

Pareidolia
Pareidolia on Mars
Images

Rotating Mask Illusion (higher cognition over-ride)


Bad Scientific Studies and Ideological "Science"
Case Study:  Vaccines Didn't Save Us
"The mythology surrounding vaccines is still pervasive, the majority of the population still believes, in faith like fashion, that vaccines are the first line of defense against disease. The true story is that nutrition and psychological/emotional health are the first line of defense against disease."

Graphs of death rates""Scientific medicine has taken credit it does not deserve for some advances in health. Most people believe that victory over the infectious diseases of the last century came with the invention of immunisations. In fact, cholera, typhoid, tetanus, diphtheria and whooping cough, etc, were in decline before vaccines for them became available - the result of better methods of sanitation, sewage disposal, and distribution of food and water."

CDC Graph

Hypothesis Testing for Vaccines vs Hygiene/Nutrition
Pre and Post Vaccine Mortality and Morbidity 

Surveillance bias
paralysis in India
Autism Rates

How To Be A Scientific Skeptic
Natural News on Milk Thistle

Bad studies:
Green coffee bean extract

Faith And Healing:  As reported
The study abstract

Subway Bread:
http://www.alternet.org/food/500-other-foods-besides-subway-sandwich-bread-containing-yoga-mat-chemical

Dosage-Response
Quantity Matters: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1770067/
Ban Water! It kills! 

Single Study Syndrome
http://scienceornot.net/2012/10/23/single-study-syndrome-clutching-at-convenient-confirmation/ 

Red Flags of PseudoScience
http://scienceornot.net/science-red-flags/
Example of applying the concepts

X Cures Cancer but the Gubmint is Hiding it!!!11!!
https://thelogicofscience.com/2016/07/04/if-cannabis-and-vitamin-b17-kill-cancer-why-arent-they-approved-by-the-fda-let-me-explain/
Key Concepts:
Elements of a Good Clinical Trial:
1)  Control Group: What success rate would we expect with chance? Unless you compare a treatment/intervention to chance/natural recovery rates there's know way to measure a treatment's efficacy.

2)  Double blinding:  You must prevent both researcher and subject bias.  Double blind is the best way to do this.

3)  A 3rd treatment Group:  With some interventions we know that just about any intervention will be better than no intervention (e.g., suicidal behavior) so rather that having only no treatment vs treatment you need to measure new treatments vs the current standard of care/drug.

4)  Objective outcome measures:  Subjective measures are often the product of psychological effects and therefore highly susceptible to cognitive bias.  It doesn't mean they are irrelevant--how people feel is an important component of health, however, someone's perception of attenuation of symptoms and whether the underlying cause is being treated are two different matters.  For this reason it's important to have objective measures of efficacy (effect on tumor size, rate of viral or bacterial reproduction, effect on size of wound, etc...)

5)  Random sample:  The sample of subject should be randomly selected.  Avoid self-selection (particularly common in weight-loss trials).

6)  Placebo control:  The control group should be given a placebo that appears indistinguishable from the actual drug/intervention/treatment.

Some Things to Look for when Evaluating a Study
1) Effect size:  If the effect size is small, it's probably due to some kind of methodological bias in the study.

2)  Duration of effect: An intervention might have only a short-term effect but claim to have a long-term effect.  This type of problem is typical in weight-loss trials.  Often interventions/treatments that only show a short-term effect are placebo. 

3)  Type of study:  Was it a pilot study? A proof of concept study? An in vitro study? Animal study? Phase 1 clinical trial?  Phase 2 clinical trial?  Retrospective study?  Longitudinal study?  FDA trial?  The lower the level the study is, the greater the chance of positive effects but this doesn't usually translate into efficacy at the human level under controlled conditions.  The more rigorous and well-controlled the study (i.e., FDA study) the more likely there is to be a smaller effect.

4)  Funding:  Be aware of the relationship between funding source, the research institution, and who gains from positive findings.  A vested interest doesn't mean the results are in valid but only that we should be extra skeptical.

5)  Reporting: How are the results being reported in the media vs what does that actual abstract say?

6)  Context:  A single study carries very little weight and so single studies should be evaluated in the context of other similar studies.  For example, if a study shows a positive result but 90% of other similar studies show no positive results, we should dismiss the one positive study (assuming it is of equal rigor).

7)  Meta-analyses:  Meta analysis are studies that combine all other similar studies on a topic to evaluate the overall trend.  The rule of thumb for evaluating meta-analysis is "garbage in--garbage out".  In other words, if most of the studies included in the meta-analysis are of poor quality then this will be reflected in the conclusion of the meta-analysis.  When evaluating meta-analysis always read the section on inclusion criteria. This will tell you what the quality benchmark was for them to consider a study in the meta-analysis. 

8) Replication:  Has the study been replicated using either the same methods or (even better) have the results been replicated using a different method?  If the answer is "no" then the results should not be viewed as definitive. 

9)  Impact Number of the Journal:  One major problem that has arisen with the internet is that anyone with a computer and a few web design skills can start an "academic journal." There are a significant number (and growing) of online journals that *look* like legitimate peer-reviewed journals but aren't.  They are ideological platforms.  If you find a journal article that seems suspicious you should (a) look up the name of the journal in wikipedia to check its origins and (b) google the name of the journal plus "impact number".  A journal's impact number is it's credibility rating.

Terminology
1) Ideomotor effect: The ideomotor effect has to do with the influence that suggestion has on involuntary or subconscious actions. In motor behavior, there are two parts to the brain activity. The first is the activity that results in the motor activity; the second is the registration of that activity in the conscious mind. The ideomotor effect happens when the second part, the conscious registration, is circumvented. RationalWiki

2) Post-hoc (after-the-fact) rationalization:  People's typically tendancy to rationalize  a belief in the face contravening evidence.

3) Placebo: A placebo is an inert substance that creates either a positive response or a negative response in a patient who takes it. The phenomenon in which a placebo creates a positive response in the patient to which it is administered is called the placebo effect.

4) Nocebo:  In medicine, a nocebo (Latin for "I shall harm") is a harmless substance that creates harmful effects in a patient who takes it. The nocebo effect is the negative reaction experienced by a patient who receives a nocebo. (Wikipedia)

Signs of Bad Science

Homework 13B
1. Pick one supplement/herbal remedy/alternative medicine treatment that you or one of your family members uses or one that you are curious about.  Find a website or article that promotes that treatment and read what their supporting evidence is.  Then go to quackwatch.com and/or http://www.sciencebasedmedicine.org/ and search for the treatment.  (If your supplement/treatment doesn't come up on one of those sites go to google and type in the name and the work "debunked").  Read the article:  what does their interpretation of the evidence suggest?  In light of the concepts we've learned today and throughout the class, write a short 1/2 page summary of your findings.  Note: try not to focus too much on the issue of biases:  focus on comparing the quality of evidence and arguments.

2. How to Make a Fad Diet. Make your own fad diet by following this handy-dandy guide

Lecture 13A: Arguments from Ignorance

Famous Thinkers Thinking about Ignorance...

"A wise man proportions his beliefs to the evidence."
--David Hume

"Extraordinary claims require extraordinary evidence."
--Carl Sagan

Arguments From Ignorance: Argument from Ignorance, Bayesian Reasoning, and the Nature of Induction

A. The argument from ignorance and its relationship to burden of proof: appropriate vs inappropriate burdens of proof, comparing probabilities.
  Example:  White raven vs black ravens

B. Conspiracy Friends: Argument from ignorance, argument from personal incredulity, anomaly hunting

  Examples and Subjects Where The Argument from Ignorance,  Is Commonly Found
Link to Unexplained Escape video

You can't prove that this isn't real...

Mumbai Weeping statue
Miracles graph
GMO safety
Ghosts/Paranormal/UFOs
Creationism
Bigfoot
Unicorns
Crop circles

Natural News On Missing Malaysian Airlines Flight
(Last section)

Pumapunku (Arg from personal incredulity, false dichotomy, arg from ignorance, arg from unqualified authority)
3:20-4:50, 5:10-5:35, 6:10-6:35, 7:30-8:00, 8:40, 12:20, 23:20-24:10
25:35 (gateway) 27:27-28:20

Puma punku AA debunk



Anomaly Hunting:
E.g., 

1. While writing for the opinion column in Utusan Malaysia's weekend edition called Mingguan Malaysia, he asked if this was the work of "certain parties" to put the nation's relations with China in "jeopardy," according to The Malaysian Insider. Furthermore, he cited facts and arguments as an evidence to support his claim of blaming the CIA for MH370 disappearance.

The editor also raised questions on CNN's role. He noted CNN was all "emotional" when Malaysia denied CIA to head the search and rescue mission for missing Malaysia Airlines Flight MH370.

Full article

Example of anomaly hunting: 
The man on the roof in the Boston bombing
The man with the umbrella at the Kennedy assassination

Summary of Important Points:
1. Formal structure of the Argument from Ignorance

Version 1

P1: There's no (good) evidence to disprove the claim.
P2: There has been a reasonable search for the relevant evidence by whomever is qualified to do so.
C: Therefore, we should accept the claim as true/more probable than not.


Version 2
P1: There's no (good) evidence to prove the claim.
P2: There has been a reasonable search for the relevant evidence by whomever is qualified to do so.
C: Therefore, we should reject the claim as improbable/false.

A strong version of an argument from ignorance has good evidence for (P2).  A weak version has little or weak support in (P2).

2. Argument from personal incredulity:  When someone argues against a claim using as support their own personal personal inability to believe or understand contravening evidence.  This is a subspecies of argument from illegitimate authority.

3. Anomaly Hunting: Closely related to motivated reasoning and backwards thinking.  The practice of ascribing evidentiary weight to randomness.

All three of these argument forms are commonly found in conspiracy thinking.



Business:
1.  Tests
a.  My grandmother's advice
b.  My proposal


Homework 13A
Part 1   Find an article/website/youtube video on one of the following: UFOs, bigfoot, the missing Malaysian airlines flight, GMOs, crop circles, ghosts, 911 conspiracy, moonlanding conspiracy, any conspiracy on http://whale.to/.  Identify at least one example of argument from ignorance or argument from personal incredulity and one instance of anomaly hunting.
Part 2  P. 258  Ex. 10B  Q2 (a), (b), (c)

Lesson 13A: Arguments from Ignorance

Introduction
The next argument scheme we will look at is what's known as the argument from ignorance.  An argument from ignorance (or argumentum ad ignorantium if you want to be fancy) is one that asserts that something is (most likely) true because there is no good evidence showing that it is false.  It can also be used the other way to argue that a claim is (most likely) false because there's not good evidence to show that it's true.

Let's look at a couple of (valid) examples:

There's no good evidence to show that the ancient Egyptians did have digital computers.  (This evaluation comes from professional archeologists), therefore, they likely didn't have digital computers.

Or

There's no good evidence to suppose the earth will get destroyed by an asteroid tomorrow.  (This evaluation comes from professional astronomers),  so we should assume it won't and plan a picnic for tomorrow.

Or

There's no good geological evidence that there was a world-wide flood event.  (This evaluation comes from professional geologists); therefore, we should assume that one never happened.

Formalizing the Argument Scheme
As you may have guessed, we can formalize the structure of the argument from ignorance:

P1:  There's no (good) evidence to disprove (or prove*) the claim.
P2:  There has been a reasonable search for the relevant evidence by whomever is qualified to do so.
C:    Therefore, we should accept the claim as more probable than not/true.
C*:  Therefore, we should reject the claim as improbable/false.

Good and Bad Use of Argument from Ignorance
The argument from ignorance is philosophically interesting because sometimes the same structure can be used to support the opposing position.  The classic example is the debate over the existence of God.  Lets look at how both sides can employ the argument from ignorance to try to support their respective position.

Pro-God Arg 
P1:  You can't find any evidence that proves that God or gods don't exist.
P2:  We've made a good attempt to find disconfirming evidence, but can't find any!
C:   Therefore, it's reasonable to suppose that God or gods do exist.

Vs God Arg 
P1:  You can't show any evidence that God or gods do exist.
P1*:  Any evidence you present can also be explained through the natural laws.
P2:  We've made a good attempt at looking for evidence of God's/gods' existence but can't find any! (I even looked under my bed!)
C:  Therefore, it's reasonable to suppose that God/gods don't exist.

This particular case brings out some important issues we studied earlier in the course such as bias and burden of proof.   Not surprisingly, theists will find the first argument convincing while atheists will be convinced by the latter.  This of course brings up questions of burden of proof.  When we make a claim for something's existence, is it up to the person making the claim to provide proof?  Or does the burden of proof fall on the critic to give disconfirming evidence?  In certain questions, your biases will pre-determine your answer.

While in the above issue, there is arguably reasonable disagreement on both sides, there are other domains where the argument from ignorance fails as a good argument.  As you might guess, this will have to do with the acceptability of P1 (i.e., there is/is no evidence) and P2 (i.e., a reasonable search has been made).  Most criticism of arguments from ignorance will focus on P2--that the search wasn't as extensive as the arguer thinks.  Generally, we let P1 stand because it is usually an authors opinion to the best of their own knowledge.  Recall from the chapter on determining what is reasonable, we typically let personal testimony stand.

We can illustrate a poor example of an argument from ignorance with an example.  Claim: There's no evidence to show Obama is American, therefore he isn't American.

Let's formalize the argument to evaluate it:
P1:  I've encountered no good evidence to show that Obama is an American citizen.
P2:  Numerous agencies and individual trained in the search and identification of state documents have been unable to locate any relevant documents.
C:   Obama isn't American (and is a Communist Muslim).

Regarding P1, maybe it's true that the arguer hasn't encountered any evidence so we'll leave it alone.  P2 however has problems. There have been reasonable searches for evidence, and that evidence was found.  Perhaps, the arguer was unaware or didn't truly exert him/herself enough.  The argument fails because P2 is not acceptable (i.e., false).

We can also typically find the argument from ignorance used in arguments against new (or relatively new) technologies in regards to safety or efficacy.  For example:

We should ban GMOs because we don't know what the long-term health effects are.

Formalized:
P1:  I've found no evidence that shows that GMOs are safe for human consumption.
P2:  Those qualified to do studies and evaluate evidence have found no compelling evidence to show that GMOs are safe for human consumption.
C:  Therefore, we should assume GMOs are unsafe and ban them until we can determine they are safe.

If we were to criticize this argument we'd consider P2.  In fact, there have been quite a few long term studies done by those qualified to assess safety.  At this point we will have a debate over quality or reliability of the evidence.  Some on the anti-GMO side dispute the reliability of the evidence (i.e., it was funded by company x, and therefore it is questionable).  In a full analysis we'd consider this question in depth, but for our purposes here, we might legitimately challenge the claim that there is no available evidence purporting to demonstrate safety.

As an aside, notice that we can also use the argument from ignorance for the opposite conclusion:  There's no compelling evidence to show that GMOs are unsafe for human consumption in the long-term, therefore, we should continue to make them available/ should not regulate them.

The "team" that wins this battle of arguments from ignorance will have much to do with our evaluation of P2: i.e., whether there legitimately is or isn't quality evidence one way or the other.

Final Notes on Arguments from Ignorance
We can look at arguments from ignorance as probabilistic arguments.  That is, given that there is little or no evidence for something, what is the likelihood that it still might exist?  This is especially true for claims that something does exist based on an absence of evidence for its non-existence.   However, as Carl Sagan famously said, "absence of evidence is not evidence of absence."  In other words, just because we can't find direct evidence for something, doesn't follow automatically that the thing or phenomena doesn't exist. There might be indirect evidence. (Of course you'd have to show this if you wanted to make this claim).

On the flip side, this style of argument can also be used to support improbable claims.  Consider such an argument for the existence of unicorns or small teapots that circle the Sun:  There's no positive evidence that unicorns don't exist or small tea pots don't circle the Sun, therefore we should assume they exist.

At this point we should return to the notion of probability:  Given no positive evidence for these claims, what is the probability that they are true (versus the probability that they aren't)?  It seems that, given an absence of evidence, the probability of there being unicorns is lower than the probability that they do not exist.  Same goes for the teapot. In both cases these are fair inferences because not only is there no direct positive evidence but there is no indirect evidence either. 

The Null Hypothesis and Burden of ProofTypically, in such cases we say that the burden of proof falls on the person making the existential claim.  That is, if you want to claim that something exists, the burden is upon you to provide evidence for it, otherwise, the reasonable position is the "null hypothesis." The null hypothesis just means that we assume no entity or phenomena exists unless there is positive evidence for its existence. In other words, if I want to assert that unicorns exist, using the argument from ignorance won't do.  It's not enough for me to make the claim based on an absence of evidence.  This is because, we'd expect some evidence to have turned up by now if there were unicorns (i.e., P2 of the implied argument would be weak).

This brings us to another Carl Sagan quote (paraphrasing Hume):  "Extraordinary claims require extraordinary evidence." Or as Hume originally said:  "A wise man proportions his beliefs to the evidence."   Claiming that unicorns exist is an extraordinary claim and so we should demand evidence in proportion to the "extraordinariness" of the claim.  This is why an ad ignorantium argument fails here;  it doesn't offer any positive evidence for an extraordinary claim, only absence of evidence. We'll discuss this principle of proportionality more in the coming section.   For now, just keep it in mind when evaluating existential arguments from ignorance.

Lecture 12A: General Causal Reasoning Part 2

Warm Up

Business
1.  Podcast/Blog Reviews:  Use the articles in Week 14 on the syllabus.
2.  Test on Wednesday:  Review after homework and short lesson.


HW Part 2:

Data:
As well as measuring the levels of current drug use, the National Household Survey also counts the number of newusers of each drug every year, in order to identify emerging trends in drug use. This "iniation rate" for marijuana increased dramatically in the early 1990s, and has remained stable since 1994.(8) In 1998, 2.3 million people tried marijuana for the first time.(9) The alltime high for new use of marijauna was in 1977, when 3.4 million people tried the drug.
The rate of cocaine use dropped dramatically between 1985, when it was as high as 3%, and 1992, when it had dropped to 0.7%. The rate did not change significantly through 1998, though there has been a slight increase since 1998.(13)
Heroin use in the United States appears to be declining slightly after an upward trend between 1992 and 1997.(17)
With the advent of "heroin chic," more people tried heroin for the first time in 1996 than in any year going back to 1970. However, new use rates have stabilized since then. A large proportion of the recent heroin initiates are young and are smoking, sniffing, or snorting heroin, rather than injecting it.(22)
Methodology and Sources of Data

Evaluate: The study found that, among both men and women, those who had used marijuana were 2.5 times more likely than those their age who abstained to later dabble in prescription drugs. (What are we measuring?)

Rand Data/Study

Things to consider:
(a)  Is the relationship causal or simply correlation? (P2)
(b)  What sorts of studies/data sets could help us make the distinction?
(c)  What is the strength of the correlation vs between alcohol and cigarettes?
(d)  Could legalization/easy access diminish hard drug use?
(e)  Measurement:  What is "used marijuana"?
(f)  Lets go through the steps for general causal reasoning...

General Causal Reasoning Con't

Study 1
Neuropathy
The Environmental Protection Agency has listed DCA as likely to be a cancer-causing agent in humans.^[47] DCA is on the California Environmental Protection Agency's list of known cancer-causing agents and is listed as a cause of reproductive harm in men.^[47][48] The International Agency for Research on Cancerclassifies DCA as a Group 2B carcinogen ("possibly carcinogenic to humans").^[49]


Echinacea and the common cold  (132million/year)


(a)

Poll

http://conservativetribune.com/sarah-palin-for-president/
http://today.yougov.com/news/2014/03/24/women-candidates/

Lecture 11B: General Causal Reasoning

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)

F.  Firearms and gun violence incidence rates. (5)
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.

M. Vaccines Didn't Save Us!






















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?

Lesson 11B: General Causal Reasoning

Introduction
Being able to separate correlation from causation is the cornerstone of good science.  Many errors in reasoning can be distilled to this mistake.  Let me preempt this section by saying that making this distinction is by no means a simple matter, and much ink has been spilled over the issue of whether it's even possible in some cases.  However, just because there are some instances where the distinction is indiscernible or difficult to make doesn't mean we should make a (poor) generalization and conclude that all instances are indiscernible or difficult to make.  

We can think of general causal reasoning as a sub-species of generalizations.  For instance, we might say that low-carb diets cause weight loss.  That is to say, diets that are lower in the proportion of carbohydrate calories than other diets will have the effect of weight loss on any individual on that diet.  Of course, we probably can't test every single possible low-carb diet, but give a reasonable sample size we might make this causal generalization. 

A poor causal argument is called the fallacy of confusing causation for correlation or just the causation-correlation fallacy.  Basically this is when we observe that two events occur together either statistically or temporally and so attribute to them a causal relationship.  But just because to events occur together doesn't necessarily imply that there is a causal relationship.

To illustrate:  the rise and fall of milk prices in Uzbekistan closely mirrors the rise and fall of the NYSE (it's a fact!).  But we wouldn't say that the rise and fall of Uzbeki milk prices causes NYSE to rise and fall, nor would we make the claim the other way around.  We might plausibly argue that there is a weak correlation between the NYSE index and the price of milk in Uzbekistan, but it would take quite a bit of work to demonstrate a causal relationship.

Here are a couple of interesting examples:
Strange but true statistical correlations

A more interesting example can be found in the anti-vaccine movement.  This example is an instance of the logical fallacy called "post hoc ergo proptor hoc" (after therefore because of) which is a subspecies of the correlation/causation fallacy.  Just because an event regularly occurs after another doesn't mean that the first event is causing the second.  When I eat, I eat my salad first, then my protein, but my salad doesn't cause me to eat my protein. 

Symptoms of autism become apparent about 6 month after the time a child gets their MMR vaccine.  Because one event occurs after the other, many naturally reason the the prior event is causing the later event.  But as I've explained, just because an event occurs prior to another event doesn't mean it causes it.  

And why pick out one prior event out of the 6 months worth of other prior events?  And why ignore possible genetic and environmental causes?  Or why not say "well, my son got new shoes 6 months ago (prior event) therefore, new shoes cause autism"?  Until you can tease out all the variables, it's a huge stretch to attribute causation just because of temporal order.  

The Argument Structure of a General Causal Claim
Someone claims X causes Y.  But how do we evaluate it?  To begin we can use some of the tools we already acquired when we learned how to evaluate generalizations.  To do this we can think of general causal claims as a special case of a generalization (i.e., one about a causal relationship).

I'm sure you all recall that to evaluate a generalization we ask

 (1) is the sample representative?  That is, (a) is it large enough to be statistically significant (b) is it free of bias (i.e., does it incorporate all the relevant sub-groups included in the group you are generalizing about.; 
(2) does X in the sample group really have the property Y (ie., the property of causing event Y to occur).

Once we've moved beyond these general evaluations we can look at specific elements in a general causal claim.  To evaluate the claim we have to look at the implied (but in good science explicit) argument structure that supports the main claim which are actually an expansion of (2) into further aspects of evaluation.  

A general causal claim has 4 implied premises.  Each one serves as an element to scrutanize.

Premise 1:  X is correlated with Y.  This means that there is some sort of relationship between event/object X and event/object Y, but it's too early to say it's causal.   Maybe it's temporal, maybe it's statistical, or maybe it's some other kind of relationship.  

For example, early germ theorist Koch suggested that we can determine if a disease is caused by micro-organisms if those micro-organisms are found on sick bodies and not on healthy bodies.  There was a strong correlation but not a necessary causal relation because for some diseases people can be carriers but immune to the disease.  

In other words, micro-organisms might be a constant condition in a disease causing sickness, but there may be other important variable causes (like environment or genetics) we must consider before we can say the a particular diseases micro-organisms cause sickness.

Premise 2:  The correlation between X and Y is not due to chance.  As we saw with the Uzbek milk prices and the NYSE, sometimes events can occur together but not have a causal relation--the world is full of wacky statistical relations.  Also we are hard-wired to infer causation when one event happens prior to another.  But as you now know, this would be committing the post hoc ergo proptor hoc fallacy.

Premise 3:   The correlation between X and Y is not due to some mutual cause Z.  Suppose someone thinks that "muscle soreness (X) causes muscle growth (Y)."  But this would be mistaken because it's actually exercising the muscle (Z) that causes both events.

In social psychology there was in interesting reinterpretation of a study that demonstrates this principle.  An earlier study showed a strong correlation between overall level of happiness and degree of participation in a religious institution.  The conclusion was that participation in a religious institution causes happiness.  

However, a subsequent study showed that there was a 3rd element (sense of belonging to a close-knit community) that explained the apparent relationship between happiness and religion.  Religious organizations are often close-knit communities so it only appeared as though it was the religious element that cause a higher happiness appraisal.  It turns out that there is a more general explanation of which participation in a religious organization is an instance. 

Premise 4:  Y is not the cause of X.   This issue is often very difficult to disentangle   This is known as trying to figure out the direction of the arrow of causation--and sometimes it can point both ways.  For instance, some people say that drug use causes criminal behaviour.  But in a recent discussion I had with a retired parole officer, he insists that it's the other way around.  He says that youths with a predisposition toward criminal behavior end up taking drugs only after they've entered a life of crime.  I think you could plausibly argue the arrow can point both directions depending on the person or maybe even within the same person (i.e., feedback loop).  There's probably some legitimate research on this matter beyond my musings and the anecdotes of one officer, but this should suffice to illustrate the principle. 

Conclusion:  X causes Y.

Premise 2, 3, and 4 are all about ruling out alternative explanations.  As critical thinkers evaluating or producing a causal argument, we need to seriously consider the plausibility of these alternative explanations.  Recall earlier in the semester we looked briefly at Popperian falsificationism.   We can extend this idea to causation:  i.e., we can never completely confirm a causal relationship, we can only eliminate competing explanations.

With that in mind, the implied premises in a general causal claim provide us a systematic way to evaluate the claim in pieces so we don't overlook anything important.  In other words, when you evaluate a general causal claim, you should do so by laying out the implied structure of the argument for the claim and evaluating them in turn.