Chapter 7: Causes Flashcards

1
Q

Causal Thinking

A
  • without conscious effort, you perceive the first event as causing the other
  • perceived causation, can be a cognitive illusion
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2
Q

An Instinct for Causal Stories

A
  • we instinctively think in terms of causal stories
  • a natural bias toward simple causal stories can lead us to oversimplify networks of causes
  • tendency to project causal narratives to natural events
  • eager to find patterns
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3
Q

Clustering Illusion

A

a form of pattern-seeking which involves expecting random distributions not to cluster

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4
Q

One Thing After Another

A
  • common fallacy to infer that B caused A just because B happened after A
  • (“after this, therefore because of this”)
  • inferring a causal connection from a sequence of events
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5
Q

Complex Causes

A
  • “What was the cause?” often has no clear answer
  • Out-of-the-ordinary factors are often said to be “the cause” of an event
  • but typically a whole network of factors was casually involved
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6
Q

Causes and Correlations

A
  • errors in reasoning happen when correlations are mistaken for causation
  • verbs like “associate”, “link”, and “relate” state correlations (but are often misread as making causal claims)
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7
Q

Three Inferential Steps

A

1) We observed a correlation between A and B

2) There is a general correlation between A and B

3) A causes B

  • strong evidence needed to make each inference
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8
Q

What Are Correlations?

A
  • if one factor occurs at higher rate when the other occurs, then the two factors have a positive binary correlation
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9
Q

Three Crucial Aspects to Correlations:

A

1) Correlations have to do with rates or proportions

2) Correlation is symmetrical

3) Correlation can be binary or scalar

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10
Q

Correlations: Symmetry

A

Symmetry: if it holds in one direction it holds in the other

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11
Q

Correlations: Binary

A

Binary correlation relates to yes/no factors

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12
Q

Correlations: Scalar

A

Scalar correlation: present when one factor occurs to a greater degree when the other occurs to a greater degree

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13
Q

Illusory Correlations

A
  • one kind of error happens at the first inferential step
  • this is when there isn’t even a correlation in but you think is
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14
Q

Reasons Behind Illusory Correlations:

A
  • motivates reasoning
  • selective noticing
  • selective memories
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15
Q

Generalizing Correlations

A
  • an error on step 2) of the three inferential steps
  • we need a large enough sample to make correlation statistically significant
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16
Q

Misleading Correlations

A
  • inferring causation is the trickiest inferential step
  • this is because correlations and so often misleading
17
Q

Reverse Causation

A
  • we can get the causal direction wrong

i.e. we think A causes B when in fact B causes A

18
Q

Common Cause

A
  • when two factors are related due to a third factor influencing both

i.e. C (third factor) causes both A and B

19
Q

Side Effects

A
  • when two factors are correlated due to some additional consequence of one of the factors

i.e. B caused by side effect of A

20
Q

Mere Chance

A
  • when a correlation in the population as a whole is merely by chance
  • correlation exists but no causal connection can be inferred
21
Q

Evidence and Experiments

A

How do we get from step 2) to step 3)? From correlation to causation?

  • our evidence must pass the strength test:

it is far more likely if A causes B than if A does not cause B

22
Q

Gold-Standard for Establishing Causation:

A
  • double-blind
  • randomized controlled trial
23
Q

Randomized

A

participants randomly divided into two groups and intervention applied to one group only

24
Q

Double-Blind

A

neither participants nor experimenters are aware of who is in which group