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Sufficiency and Necessity Assumptions in Causal Structure Induction.

Ralf Mayrhofer¹, Michael R Waldmann¹

¹Department of Psychology, University of Göttingen.

Cognitive Science

|November 3, 2015

Summary

People utilize stable, yet variable, causal beliefs to infer cause-and-effect relationships. These priors influence structure selection and can be experimentally modified, suggesting domain-general application in causal learning.

Keywords:

Bayes nets Causal induction Causal learning Structure induction

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Area of Science:

Cognitive Psychology
Causal Inference

Background:

Human causal induction research reveals prior assumptions about causal strength and interactions.
These priors influence estimations of causal strength and cause selection.

Purpose of the Study:

To investigate if prior assumptions about causal parameters influence the selection of alternative causal structures.
To determine the stability, variability, and domain-generality of these priors.

Main Methods:

Three experiments were conducted where participants chose between two observable variables as cause and effect.
Priors regarding causal determinism (sufficiency or necessity) were assessed.

Main Results:

Learners exhibit interindividually variable but intraindividually stable priors about causal parameters.
These priors demonstrate a preference for causal determinism and predict structure selection.
Priors were experimentally manipulable and appeared domain-general.

Conclusions:

Prior assumptions about causal parameters play a crucial role in selecting causal structures.
Heuristic strategies in structure induction may represent simplified implementations of these priors.