Elena Popa
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Children’s causal learning and evidence.
Causation, intervention, and Bayes nets.
The conditional intervention principle and
Woodward’s concept of an intervention.
Conclusions: Connecting causality and
evidence with intervention – causal learning.
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‘blicket-detector’ A&B activate the machine;
separately: A activates the machine, B does
not. A is a blicket. (3 and 4 year olds)
Probability: ‘… even very young children can
and do infer new causal relations from
information
about
dependent
and
independent probability.’ (Gopnik et al 2001:
628)
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Causes as increasing probability: A activates
machine 3 times in a row; B only activates it
twice in 3 trials. While most of the children
count A as a blicket (97%), a considerable
number (85%) take B to be a blicket as well.
Bayes nets enable predictions about the effect
on intervention (intervening on the cause
leads to a change in the probability
distribution of the effect)
Children making causal claims and updating
them in the light of evidence
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Children establishing the causally-relevant
variables through interventions
Children’s free play – generate evidence to
support causal learning and learn from the
evidence of their own interventions (Schulz et
al. 2007: 330)
Causal learning both in the laboratory and
real-world scenarios.
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Children presented with gears A and B (of
different colours) and a switch S;
They are shown pictures with the causal
structure (common cause or causal chain);
They should be able to choose the right
pictures upon being presented with evidence
of how the mechanism works upon having
one gear removed at a time while the switch
is on;
In experiment 3 they were able to do so by
working with the gears themselves (Schultz et
al. 2007: 324);
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Holding all the other variables in the system
fixed.
An intervention on X that
Will change the probability distribution of Y
But not influence Y other than through X
And not change the fixed values of the other
variables in the graph.
(after Schulz et al. 2007: 323)
C causes E – variables; if C were to be changed,
E would change as well.
Woodward’s concept of an intervention
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The intervention works as a switch;
If the interventions by the experimenter are
correlated with other causes of recovery than
T (e.g. placebo), then the reliability of the
experiment is undermined.
The intervention should not affect recovery
independently from T, but, if at all, through
it.
(M1) I must be the only cause of X; i.e. (…) the
intervention must completely disrupt the causal
relationship between X and its previous causes so
that the value of X is set entirely by I,
(M2) I must not directly cause Y via a route that does
not go through X (…),
(M3) I should not itself be caused by any cause that
affects Y via a route that does not go through X,
and
(M4) I leaves the values taken by any causes of Y
except those that are on the directed path from I to
X to Y (should this exist) unchanged. (Woodward
2008; also Woodward 2003 for a more detailed
account)
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2.
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4.
5.
Holding all the other variables in the system
fixed.
An intervention on X that
Will change the probability distribution of Y
But not influence Y other than through X
And not change the fixed values of the other
variables in the graph.
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Intervention
Variables
Probability-increasing
Bayesian evidence
Counterfactuals
Do people learn and reason in accord with the
normative requirements of the interventionist
account? (Woodward 2007: 28) –Yes
Does the interventionist account help in designing
experiments involving causal learning in
children? –Yes
Does the data about children’s intuitive theories
and causal learning tell us something about the
use of interventions in full blown scientific
theories? - Yes
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Experiment results and causal learning in
children – consistent with both interventionist
and probabilistic approaches to causation;
Why intervention? – generating evidence
through interventions, learning through
interventions;
Compatible with a Bayesian approach to
evidence;
The use of interventions from early childhood
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supports
connecting
causation
to
manipulability;
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The
theory-theory
of
conceptual
development: interventions should be helpful
when investigating causes in both children’s
theories and in scientific theories
Should causation be analyzed mainly in terms
of manipulability? – Woodward’s investigation
on interventionist theories in psychology;
Metaphysical issue: how we learn about
causes vs. what is the fundamental concept
underlying causal claims.
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Gopnik A., Sobel, D., Schulz, L., Glymour, C. (2001)
‘Causal learning mechanisms in very young children:
Two-, three- and four-year-olds infer causal relations
from patterns of variation and covariation’,
Developmental Psychology 37: 620-629.
Glymour, C. (2000) ‘Bayes nets as psychological
models’, in Keil, F. & Wilson, R. (eds), Explanation and
Cognition, Bradford Books.
Schulz, L., Bonawitz, E., Griffiths, T. (2007) ‘Can being
scared cause tummy aches? Naïve theories, ambiguous
evidence and preschoolers’ causal inferences’,
Developmental Psychology 43: 1124-1139.
Schulz, L., Gopnik, A., Glymour, C. (2007) ‘Preschool
children learn about causal structure from conditional
interventions’, Developmental Science 10: 322-332.
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Woodward, J. (2003) Making Things Happen,
Oxford University Press.
Woodward, J. (2007) ‘Interventionist theories of
causation in psychological perspective’, in Gopnik,
A., Schulz, L. (eds), Causal Learning, Oxford
University Press.
Woodward, J. (2008) ‘Causation and Manipulability’,
The Stanford Encyclopedia of Philosophy (Winter
2008 Edition), Edward N. Zalta (ed.),
plato.stanford.edu/entries/causation-mani/ .