lecture 11

Question 1

why bother about causation

Answer 1

understanding causes matters, because it allows us:

To intervene and predict, so that we can avoid mistakes (as much as possible) and delive desired outcomes instead

To explain why or how things happen

Question 2

Philosophers have typically worried about 2 sets of questions regarding causality

Answer 2

1) how can we make reliable causal inferences

2) What is causality

Question 3

What is causality

Answer 3

No agreement among philosophers

still different theories on the table

Causality is…
regular association
difference-making and manipulability
energy transference
dispositions / tendencies

Question 4

Causality is…

Answer 4

Regular association
Difference making and manipulability
energy transference
Dispositions/tendencies

Question 5

How can we make reliable causal inferences

Answer 5

Scientists use experiments and models to investigate causal relations

Question 6

How can we make reliable causal inferences (take home message)

Answer 6

Many alternative metaphysical accounts of causation

Still we have a meaningful discourse about how to make reliable causal inferences by using a minimal account of causation

This minimal account of causation includes the notions of causality as difference-making (or counterfactual reasoning) and as manipulability (or intervention)

Question 7

Association

Answer 7

two variables C and E are associated (or dependent) when one variable provides information about the other

Question 8

Correlation

Answer 8

two variable display an increasing or decreasing trend

Question 9

Causation

Answer 9

many possible definitions; here we go with a minimal one: association + manipulability

Question 10

Temporal succession

Answer 10

C regularly comes before E

Question 11

Contiguity

Answer 11

C and E happen nearby in space

Question 12

Conditional dependence

Answer 12

P(EIC) != P(E) and P(CIE) != P(C)

Question 13

which kind of information we get from association

Answer 13

Causes and effects are discoverable not by reason but by experience

Question 14

Problems with regular association view

Answer 14

1) some variables are causally related, but not (spatially or temporally) contiguous

E.g. The lockdown in china caused a drop of oil prices in saudi arabia, though the two events are not spatially (or temporally) contiguous

2) causal relationships are asymmetric

C causes E but E does not cause C

E.g. The lockdown in china caused a drop of oil prices in saudi, but the drop of oil prices in saudi did not cause the lockdown in china

3) problems with regular association view

Some variables are conditionally dependent/associated/correlated but not causally related

E.g.Whenever ice cream sales are high, incidence of drowining is also high. Do ice creams cause drownig? no! both are effects of a common cause, namely warm weather

Question 15

Manipulability

Answer 15

two variables C and E are causally related when, if the value of C changed, the value of E would change too

Basic idea: If C and E are only associated or correlated, then intervening on C will not change the value of E

Question 16

what is a common cause

Answer 16

An observed dependence between two variables X and Y is indicative of either X causing Y or Y causing X or the existence of a COMMON CAUSE Z

Question 17

What is a common cause

Answer 17

in the case of ice cream consumption and drowining rates, both have a common cause namely hot weather

However that hot weather is a common cause of these two events is an inference

Hot weather works here as a commmon cause which explains the existing relationship between two events

Question 18

Common causes and bayesian networks

Answer 18

The inference from the association of x,y to the common cause z will be assigned a probability value

The causal links z -> x and z -> y can also be assigned a probability value

There will be background assumptions justifying x,y -> z

Question 19

In complex situations, one can use bayesian networks/ causal models to

Answer 19

make the assumptions behind causal inferences explicit

Ask justification for any particular set of assumptions, and investigate what happens if an assumption fails

Develop algorithms for causal discovery