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Causation in Populations: Exercise 2-2
Exercise Context:
Let's consider another effect and some of the causal factors that might be related to it. The effect is a car battery losing its charge. The potential causal factors are: a) leaving the headlights on for over two hours (or not), b) the age of the battery being over two years (or not), and c) there being an electrical short in the internal plates of the battery (or not).
Let's define the causal structure in this case by using a table:
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Situation
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Causal Factor 1
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Causal Factor 2
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Causal Factor 3
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Effect
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1
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Lights on 2 hours
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Over 2 years old
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Short in plates
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Loses charge
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2
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Lights on 2 hours
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Over 2 years old
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No short
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Loses charge
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3
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Lights on 2 hours
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Not over 2 years old
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Short in plates
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No charge lost
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4
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Lights on 2 hours
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Not over 2 years old
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No short
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No charge lost
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5
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Lights not on
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Over 2 years old
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Short in plates
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Loses charge
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6
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Lights not on
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Over 2 years old
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No short
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No charge lost
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7
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Lights not on
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Not over 2 years old
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Short in plates
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No charge lost
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8
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Lights not on
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Not over 2 years old
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No short
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No charge lost
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Here is another table that dislays the percentages of car batteries in a population in each of four of of the possible causal situations:
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Situation
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Percentage in the Situation
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2
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5%
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4
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10%
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5
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5%
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8
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80%
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This kind of account of the percentages of a population that have some properties (in this case a causal situation) is often known as a "distribution" of the properties. If we assume that all batteries in all cars are governed by the partial causal structure given in table X and that causal structure has all the relevant causal factors, then in the population described above, 10 % lose their charge.
This is an applet that allows you to vary the distribution of causal situations in the population of car batteries.
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Question:
Create a population of batteries that displays the following features:
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01 |
All eight causal situations are represented in the population
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02 |
15% of the batteries will lose their charge
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