chapter 6. establishing causation it appears that lung cancer is associated with smoking. how do we...

Chapter 6

Establishing causation

It appears that lung cancer is associated with smoking.

How do we know that both of these variables are not being affected by an unobserved third (lurking) variable?

What if there is a genetic predisposition that causes people to both get lung cancer and become addicted to smoking, but the smoking itself doesn’t CAUSE lung cancer?

1) The association is strong.

2) The association is consistent.

3) Higher doses are associated with stronger responses.

4) Alleged cause precedes the effect.

5) The alleged cause is plausible.

THERE IS NO SUBSTITUTE FOR AN EXPERIMENT!!!

We can evaluate the association using the following criteria:

64% of American’s answered “Yes” . 38% replied “No”. The other 8% were undecided.

Cause: An explanation for some characteristic, attitude, or behavior of groups, individuals, or other entities

Causal effect: The finding that change in one variable leads to change in another variable, other things being equal.

3 required 1.Association: Empirical (observed)

correlation between independent and dependent variables (must vary together)

2. Time Order: Independent variable

comes before dependent variable

3. Nonspuriousness: Relationship between independent and dependent variable not due to third variable

These two strengthen the causal argument

4. Mechanism: Process that creates a connection between variation in an independent variable and variation in dependent variable

5. Context: Scientific explanation that includes a sequence of events that lead to particular outcome for a specific individual

• Can not be used to explain general ideas, places, events, or populations

Correlation tells us two variables are related

Types of relationship reflected in correlation:

X causes Y or Y causes X (causal relationship)

X and Y are caused by a third variable Z (spurious relationship)

7

‘‘The correlation between workers’ education levels and wages is strongly positive”

Does this mean education “causes” higher wages?We don’t know for sure !

Correlation tells us two variables are related BUT does not tell us why

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Possibility 1 Education improves skills & skilled workers get better paying jobsEducation causes wages to

Possibility 2Individuals are born with quality A, which is relevant for success in education and on the jobQuality A (NOT education) causes wages to

9

Kids’ TV Habits Tied to Lower IQ Scores

IQ scores and TV timer = -.54

Eating Pizza ‘Cuts Cancer Risk’

Pizza consumption and cancer rate

r = .-59

Reading Fights Cavities

Number of cavities in elementary school children & their

vocabulary sizer = -.67

Stop Global Warming: Become a Pirate

Average global temperature and number of pirates

r = -.93

A strong relationship between two variables does not always mean that changes in one variable causes changes in the other.

The relationship between two variables is often influenced by other variables which are lurking in the background.

There are two relationships which can be mistaken for causation:1. Common response2. Confounding

Common response• Possibility that a change in a lurking

variable is causing changes in both explanatory variable and response variable

Confounding• Possibility that either the change in

explanatory variable is causing changes in the response variable

OR• That change in a lurking variable is causing

changes in the response variable.

Both X and Y respond to changes in some unobserved variable, Z.

The effect of X on Y is indistinguishable from the effects of other explanatory variables on Y.

Example of confounding: The “placebo effect”

When controlled experiments are performed.

When can we imply When can we imply causation?causation?

Strongest for demonstrating causality

Asch Experiment https://www.youtube.com/watch?v=F17JGDZDVUs

Quasi-experimental designs Looks like experimental design but lacks -- random assignment

Attraction and Scary Bridge https://www.youtube.com/watch?v=YLXFmQEF

mn0

Most powerful design for testing causal hypotheses

Experiments establish:AssociationTime orderNon-spuriousness

Two comparison groups to establish associationExperimental Group:

Treatment or experimental manipulation

Control group: No treatment

Variation must be collected before assessment to establish time order

Post-test: Measurement of the DV in both groups after the experimental group has received treatment

Pre-test: Measurement of the DV prior to experimental intervention True experiment doesn’t need a pre-test Random assignment assumes groups will

initially be similar

Random assignment (randomization):Of subjects into experimental and control groups

Establishes non-spuriousnessNot random samplingRandomization has no effect on generalizability

Assignment of subject pairs into experimental and control groupsBased on similarity (e.g., gender, age)

Individuals (in pairs) randomly assigned to each group

Can only be done on a few characteristicsMay not distribute characteristics between the two groups

Establish time order & association

May be better at establishing context

Cannot establish non-spuriousness

Comparison groups not randomly assigned

Confidence in cause and effect relationship

Key question in any experiment is:

“Could there be an alternative cause, or causes, that explains the observations and results?”

Generalization: Whether results from small sample group, in a laboratory, can be extended to make predictions about entire population

Threats to validity in experiments

True experiments have high internal but low external validity

Quasi-experiments have higher external but lower internal validity

Experimental and Control groups are not comparableSelection bias: subjects in experimental

and control groups are initially different

Mortality/Differential attrition: groups become different because subjects are more likely to drop out of one of the groups for some reason

Instrument decay: Measurement instrument wears out or researchers get tired or bored, producing different results for cases later in the research than earlier

Natural developments in subjects, independent treatment, account for some or all of change between pre- and post-test scores

Generally, eliminated by use of control group

Changes same for both groups.

Testing: Pre-test can influence post-test scores

Maturation: Changes may be caused by aging of subjects

Regression to the mean: When subjects are selected based on extreme scores

In future testing: Regress back to average

Things happen outside experiment may change subjects’ scores

Control and experimental groups affect one another

Demoralization:

The control group may feel left out and perform worse than expected

Compensatory Rivalry (The John Henry Effect):

When groups know being compared

May increase efforts to be more competitive

Expectancies of Experimental Staff:Staff actions and attitudes change the behavior of subjects (i.e., a self-fulfilling prophecy)

Resolved by double-blind designs Neither the subject nor the staff

knows who’s getting the treatment and who’s not

Placebo Effect: Subjects change because of expectations of change, not because of treatment itself

Hawthorne Effect: Participation in study may change behavior simply because subjects feel special for being in the study

More artificial experimental arrangementsGreater problem of sample generalizability

Subjects are not randomly drawn from population

Field experiments: Conduct experiments in natural settings Increases ability to generalize.

Random assignment is critical