Presentation on theme: "Elena Popa. Children’s causal learning and evidence. Causation, intervention, and Bayes nets. The conditional intervention principle and Woodward’s."— Presentation transcript:
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.
- ‘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)
- 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
- 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.
- 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);
1. Holding all the other variables in the system fixed. 2. An intervention on X that 3. Will change the probability distribution of Y 4. But not influence Y other than through X 5. 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
1. The intervention works as a switch; 2. 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. 3. 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)
1. Holding all the other variables in the system fixed. 2. An intervention on X that 3. Will change the probability distribution of Y 4. But not influence Y other than through X 5. And not change the fixed values of the other variables in the graph.
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
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 – supports connecting causation to manipulability;
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.
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: 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: Schulz, L., Gopnik, A., Glymour, C. (2007) ‘Preschool children learn about causal structure from conditional interventions’, Developmental Science 10:
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/.