Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study

Caren A Frosch,Teresa Mccormack,Patrick Burns,David A Lagnado

doi:10.1111/j.1551-6709.2011.01208.x

Abstract

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children's causal structure and intervention judgments were consistent with one another. In Experiment 1, children aged between 4 and 8 years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children's causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked.

Highlights

The application of the formal framework of causal Bayesian Networks to children‟s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system
If the participant identified the causal chain structure illustrated in Figure 1b (A causes B and B causes C) as the one that shows how the apparatus operates, their responses to the two intervention questions should differ
Three objects were used in this experiment because participants were shown a single causal system; these objects were a blue ball sitting on top of a bent spindle which rotated in the horizontal plane along an elliptical pathway, a yellow square (6 cm x 6 cm) and a red cylindrical bar (10.5 cm long and 2.5 cm in diameter)

Summary

Introduction

The application of the formal framework of causal Bayesian Networks to children‟s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. As Hagmayer et al (2007) have demonstrated, because causal Bayes nets summarize conditional probability information, they can be used flexibly to generate predictions about what should happen to a variable in a system if another one is manipulated (given additional assumptions about the nature of causal models) This is held to be one of the major strengths of this approach (see various contributions to Gopnik & Schulz, 2007), and it leads to the prediction that what is learned about the relationships between variables in a causal system should be able to support judgments about the hypothetical or counterfactual effects of intervening on (manipulating the value of) its variables. As far as we understand it, a strong interventionist account of causal representation holds that what it is to represent a system as (e.g.,) a causal chain rather than a common cause structure just is to be committed to these differential effects of intervening on these variables

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Conclusion