1. Concepts to be included
(causal) hypothesis
Bivariate
Explanation/confounding (effect of a ‘third’ variable)
Confounding variable
Specification/interaction/modification
Modifier (interaction variable)
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4/29/2019

2. The effects of third variables
Henk van der Kolk

3. Aim
Understanding the potential effect of ‘third’ variables (spurious relationships)
I discuss two effects:
Explanation/Confounding
Specification/ Interaction/ modification

4. Two examples
Why are more babies born (per capita) in some municipalities than in others? (to illustrate ‘confounding’)
Why do some people spend more on holidays than others? (to illustrate ‘interaction’)

5. Three aspects of causality
Cause precedes the consequence in time (correct time order)
Consequence occurs less often if the cause is absent (association)
No common cause of cause and consequence (more generally: no third variable affecting the relationship) (no spurious relationship)

6. Spurious relationships
Relationships can be ‘spurious’ or seriously biased because of …
Explanation / confouding
Specification/ interaction/ modification.
‘Third variable bias’.
De puntjes vallen nu precies op de volgende regel. De zin net een woordje langer of korter maken

7. Example I: confounding
In municipalities with a large number of storks per capita, the number of children per capita is relatively high, while in municipalities with a small number of storks, the number of babies is relatively low”

8. The expectation in a model
Number of storks
Number of babies

9. The expectation in a graph
Babies
Storks

10. Testing the causal relationship
Babies causing storks? (correct time order)
Correlation? (we found an association)
What would be the ‘theoretical argumentation’ about why this is still NOT a causal relationship?

11. Degree of urbanization
Confounding in a model
Degree of urbanization
Number of storks
Number of babies

12. Confounding in a graph Not urbanized (1) Babies Urbanized (3, low)
Urbanized (5, medium)
Urbanized (7, high)
Urbanized (10, highest)
Storks

13. Example II: interaction/Specification
‘Households with a high level of income, will spend relatively more on holidays, than people with a low level of income.’

14. Holiday spending Income
Units: households Variables: income & holiday spending Sign: positive

15. Spending for holidays
Income

16. Testing the causal relationship
Holidays causing income? (correct time order)
Correlation? (we found an association)
What would be the ‘theoretical argumentation’ arguing why this is NOT a simple causal relationship?

17. Willingness to go on holiday
Interaction in a model
Modifier variable
Willingness to go on holiday
Income
Holiday spending

18. Interaction in a graph Wants to go Spending for holidays
Does not want to go
Income

19. This microlecture
Understanding the potential effect of ‘third’ variables Confounding (aka ‘Explanation’) Interaction (aka ‘Modification’ & ‘Specification’)

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21. Images used
Slide 7:
Slide 13: