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- Counterfactual Causal
- Cause is shown by the contrast of supposed the same population but one with and one with no exposure. A difference show it is cause. No difference show it is not a cause. A proxy for causal contrast is to use a control group, but use different groups of people.
-Quantification:
1. Risk: Start with non-disease of interest, follow over time and observe for outcome. Proportion of people in fixed cohort who develop the disease over a specific time period. This is most intuitive and fundamental but hard to obtain.
2. Rate: Got disease/person time; (would incidence rates be considered a rate even though it is not a true rate?)
3. Odds: A probability divided by its complement
4. Prevalence: We want proportion of people any moment in time. This can capture people who recovered.

Basic Comparison Used in Epidemiology: Risk and Difference
A. Ratio: This is used more in epidemiology.
1. Risk ratio: a/(a+b)/c/(c+d)
2. Rate ratio: a/(a+b)-c/(c+d)
3. Odds ratio: ad/bc
B. Difference:
1. Risk difference
2. Rate difference
3. Odds difference

Fleiss, 1981
Rate of lung cancer for smoker and nonsmokers; rates of CAD for smoker and nonsmoker. Lung cancer rate is 11 (smoke vs. nonsmoker). CAD is 2. Looking at this, rate ratio would let you think it is important for lung cancer. But then the rate difference is greater, because CAD is more common.
-Ratio is more used because: chronic disease shift. There was a problem in chronic disease because the way causes for chronic disease is different than what was there for infectious disease. There was a question whether smoking was cause for lung cancer. Because the ratio look more impressive, it was pushed to be used a paradigm. There were social historical constraints that made risk more used. Shows:
1. Epidemiological method are developed for population in need at particular time.
2. Counterfactual definition of the cause suggest difference measure might have more validity especially when we talk about interaction.
Both get you causal contrast but tell you different things.

Health Disparities
Risk ratio of poor vs rich is 3 in Time 1. Then in Time 2, risk of poor vs poor is 8.
Risk difference in T1 is 0.4. But then in T2, difference is 0.007.
Reason:
1. 60% of people in T2 had disease.
2. But then the intervention lowered for both, but comparison between the rich and poor is still different after getting better. The poor population just still need more work.
Both are fine and acceptable but they can give a different feeling for the same data.
Article will be posted on the webpage.

Risk in Exposed vs. Risk due to the Exposure
Example: We had identified 3 casual pies. We have E and G in Pie 1. Pie 2 has L. Pie 3 has P. Any pie can lead to disease.
Population 1 all have E. Over time, people who get pie will get disease.
Person 1,3 got the disease due to the exposure because if they didn't have E, then they would not have had disease.
Person 2 got the disease because they got L so they didn't get the disease from the exposure. W/o E they still out have gotten disease.
Person 5 also did not get disease from exposure because they got from exposure pie P.
4 people got disease, but only 2 got because of exposure.
With these people you can do a risk ratio with people who got disease from exposed vs. unexposed. Whatever the INUS cause is, we need to compare the rate of disease with or with no INUS cause.

Our comparison is only has good as our proxy for our counterfactual. Therefore we can only infer causation.

Goal of Epidemiology Studies is to make causal inference as strong as possible. At each step of the counterfactual to inference, we want to make it as strong as possible.
Step 1: Make question strong for causal inference: develop elaborated causal theory.

Two Approaches to Causal Inference
1. Induction: Look at specific facts in real world. From facts, you develop a theory/generalized principle. Ex. See white swan today. See white swam tomorrow. Induce that all swans are white.
2. Deduction Start with theory and make prediction on specific incidence. Ex. All men are mortal. Sophocles is a man. Therefore Sophocles is mortal. Stress causes disease. Individuals under stress will get depression.

Danger of Induction: Torture the data and it will confess. No logical limitation to your explanation. Whatever you see, you can explain the trend no matter what you see. Ex. Fat Jeans- they fit anything. All you get is association but you never get causation. Problem is that you can't intervene on association and only causation. So we can't be satisfied with association. In opposition to address this problem from induction, people think about eliminate inadequate theories.
1. We expect our data results under theory and then see if the results match or no. We eliminate the inadequate or not logical reason in the first place.
2. We can also eliminate inadequate theories by falsifying the data. We need to protect self from having a bias about theory which leads us to interpret data a certain way. Having a frame of what will falsify theory will help make your theory. More falsify you make your theory, when it doesn't get knocked down at the end of the data means your theory was correct. Problem: we never see facts. We want to see how much disease in exposure group, unless we measure the whole population we don't get a fact. There are constraints on what you can imagine also. Ex. In 1950, ulcers were thought to be caused personality. While data show it was due to infection, but because paradigm was there so people couldn't see it. Ex. Fertilization was changed when women went in field. Egg was waiting for fertilization, but in real egg does things. Facts are refracted by the way we think. Our attempt to falsification is fallible. When we going through study, we want to make sure we are not careless and things are falsify-able because things can go wrong in study.
The question is if we should start with induction or deduction.

Develop Causal Theory
1. Be open; get ideas from everywhere. Dissertation can come from anywhere. Once you have idea, then it makes it seem like everyone is talking about your idea.
2. Know your stuff: read broadly. Read in particular literature of people who don't agree with you.
3. What's bugging you? Keep diary of what you are reading and your reaction towards it. Keep diary or notes can allow you what your really thinking about or bothering you: things that are contrasting facts. You have idea and then develop theory with idea.

Hills's Causal Criteria:
A. Begin with Hill's Criteria help you in the development of your theory. Things to consider:
- Temporality: example women more prone to depression due to menopause but depression starts from teenage years.
- Plausibility: why would exposure and disease be linked in the first place?
- Coherence: can you make a good story out of your theory. For instance, you didn't do hw for a class. You are asked why did you do your hw? Logic thing so have one thing to explain.
- Analogy: Susser and schizophrenia about whether it is a neurodevelopment disorder? Or is it a neurodegenerative disorder? This hypothesis came because of neural tube defects. They found neurodevelopment problems due to nutrient defects. So it was thought that in utero that cause other neurodevelopment disorder. So look at schizophrenia.
B. Evidence for theories:
- Consistency
- Experimental evidence.
C. Evidence regarding specific corollaries
- Strength
- Biological gradient
- Specificity: one bacteria, one disease. But then environmental exposure, we don't find this. Direct cause/relationship so in study we need to ask ourselves to whether we should expect specificity.

Elaboration of Causal Theories (Shadish, Cook, and Campbell): These are all defined different ways.
Causal Identification: Isolate the cause of interest from other causes. Two component:
A. Association (main effect)
B. Eliminate Competing hypothesis (internal validity-eliminate competing hypothesis): confounding
Causal Explanation: Explain how and when the cause of interest acts.
C. Explain how exposure works (construct validity): mediation
D. Explain when exposure works (external validity): effect modification

Example:
Independent risk factors=Smoking is INUS cause of CAD and Genetic cause CAD. Each are a cause.
Confounding= Competing explanation: Hormones and social roles are cause of depression.
Mediation=Low SES leads to exposure to environmental toxin and then cancer; chain of events.
Effect modification=together work together to cause disease

Causal Identification: footprint that causes leave behind. You can never see causes.
-Association beyond chance
-Temporal order
-Sole plausibility

There are only 5 reason why causes lead to disease
1. Chance (association challenges this)
2. Reverse causation (temporality challenges this)
3. Bias (sole plausibility challenge this)
4. Confounding (sole plausibility challenge this)
5. Direct cause
So, we want to eliminate all other factors, and the one which remains must be true.

DAGS: Diagrams and arrows mean causality. X-->D; X is INUS cause of D.
X--> D
E--> D
E is cause of interest. X is whole pie and is independent.

Confounding:
X--> D
X-->E
X is a INUS cause of D. But X is also a cause of E. So it makes E and D associated as a result of common cause. But E does not cause D.

Mediation:
E-->X--> D

Effect Modification
(X and E)-->D
The presence of each other makes disease.
Venn Diagram E and X is one way of showing effect modification.

Developing a Causal Theory and Hypothesis about Obesity
TV watching is INUS cause of obesity=theory.
Main effect: Obesity is caused by increase caloric intake.
Knock out confounding: not because of couch potatoes effect (no exercise)
Mediation: Increase TV lead to increase one eating food.
Effect Modification: When does it work: when at least one parent is obese.
These are components but mediators might not happen.

Goal: To have a coherence and parsimony theory. Specificity and complexity in your predictions.

Example:
Depression is associated with loss of significant relationship, with no replacement. Then if that is the case, it is parsimonious and falsifiable, you would see many of the other studies. The question is when to abandon or when is it to change theory.

How do you defend your viability of your "casual theory"? Aka Literature Review
Background and Significance as Principled Argument (Craft of Research) Talks about Principled Argument.
Claim, Evidence, Warrant (what are the implications as a result of the evidence to the claim) that links Claim and Evidence
Then what are the qualification?

Thesis: Not all Claim are Acceptable. Need to be
Substantive (about something important)
Contestable (challenges a belief)

Strong Evidence has to be
Accurate (no careless errors and fully recognizes limitations of the evidence)
Authoritative (what counts as evidence in epidemiology)

Strong Warrants:
What general belief must I have before I can agree that your accurate evidence supports your claim?

Strong Qualification:
Without Concessions-
-Raise objections and alternatives you considered and rejected
-Anticipate objections
-Anticipate alternatives.

(couple more points missing)

Mystery of the Week:
Noncomparability....

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