# Lecture 016

Objection:

• there exists no causal (interpretivists)

## Reading

### Democratic Peace Theory

democratic peace: democratic countries do not fight each other

Reason for causation: we want to control saturation

Critiques:

• not a causation study

• it demonstrates democratic peace, not "democrat countries are more peaceful"

• hard to opeerationalize "war" and "democracy"

• should test if correlation hold on larger dataset

### Law and Correlation

Difference between law and correlation:

• Law: general, no reference to particular objects, time, or places

• Law: support counterfactual statements (predictive?)

### Creativity

Do (causal) social law exists?: it exists if one believes social science theory are generalizable. When human have "creativity"

• How do you measure creativity?

• Quantum physics can generate "real random" much like definition of "creativity"

Do human have creativity

• Yes: creativity from free-well

• No: creativity should be something entirely new, doesn't exists in society. One would justify something is a innovation if the innovation should happens at a later time, but this justification is self-defeating by create the innovation.

Social laws can be formulated in another vocabulary (like physical science)

Is human predictable: Consider chaos theory

• Yes:

• it has underlying law: chaos theory
• it has no underlying law
• No: (it is not predictable in terms of current technology)

### Causality

Most famous definition

Hume's causality: an object followed by another, and where all the objects, similar to the first, are followed by objects similar to the second. Or, in other words, where, if the first object had not been, the second never had existed.

Critiques:

• first sentence: empirical regularity, true generalization

• second sentence: counterfactual

### Probabilistic dependency

There is no causality in linear regression

### Interventionism

Thomas Reid's causality: controlled change of variable limitation

• only done so by human (because in this case we assume human have free well)

• can't go back into history (modality assumption)

• can't identify which level is the true cause if applied reductionism

• isolation of confounding variable

adjusted definition: A necessary and sufficient condition for X to be a direct cause of Y with respect to some variable set V is that there be a possible intervention on X that will change Y (or the probability distribution of Y ) when all other variables in V besides X and Y are held fixed at some value by interventions.

Two advantage

• interventionist can give causality without given law

• genuine(true) and spurious(false) generalizations

## Questions

1. policy recommendations need causal claims to justify. (Justifying spreading of Democracy). The need for causation is because we want to control things.

2. interventionist vs. "regularity account"

3. a natural law: in its definition, should be context-less

4. interventionist can rely on exact context to produce "imagined" outcome Since we can't "observe causality", we need to establish a standard to decide whether a causal statement has "causality credibility"

5. regularity account: gains its credibility by generalization

6. interventionist: gains its credibility by interventionist

7. Justification for linear model?

8. it might be non-linear

## - it might not be caused

1. Firstly, this linear equation model disregards the correlation coefficient, making the equation a non-probabilistic model. In a non-probabilistic model, the model seems to suggest that if I plug variables (my years of education, my score on the last state exit exam, etc.) into the equation, I expect to get the exact result (the exact number of my income). Since this is rarely the case, the equation is a bad explanation of influences on income. Secondly, the linear equation model seems to suggest a correlation than causation because the model is likely created by individual data points collected from uncontrolled observations. One might interpret this linear equation as one spends more years in an educational system causes the person to receive a higher income. But this causality does not necessarily hold because one can argue that a person with a higher income allows the person to spend more years in the educational system. Thirdly, even if causality in the study is supported by good experimental practice using interventionist methods, one who believes in human's free-well may argue that the income of one person can't be caused by variables like "Years of Education" or "Score on last state exit exam take" since it is ultimately the boss's psychology that makes his act of giving a person certain money.

## Class

Regularity: let X in set XS, Y in set YS, for all X and Y...

• definition

• when X happens, Y happens
• when X happens, increases probability of Y happening
• X before Y
• X and Y be "contiguous" (near each other in space-time)
• limitation: not actual cause, interference, don't capture full picture

Interventionist: a definition in terms of intervention

• challenges
• one must believe in human's free-well to take interventionist view (are there other event caused scientists to do the experiment)
• cannot evaluate historical event, since they are not intervene-able

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