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Causal intuition in the indefinite world: Meta-analysis and simulations
Kohki Higuchi1, Kuratomo Oyo2, Tatsuji Takahashi1
1School of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hiki, Saitama 350-0394, Japan.
We introduce a new causal induction model, proportion of assumed-to-be rare instances (pARIs), which excels in the initial causal discovery stage. This model demonstrates superior performance in data fit and population covariation detection compared to existing methods.
Area of Science:
- Cognitive Science
- Psychology
- Artificial Intelligence
Background:
- Causal intuition is key to understanding behavior.
- Existing causal induction models have limitations.
- The two-stage theory of causal induction provides a framework for analysis.
Conclusions:
- pARIs is a leading model for the first stage of causal induction (causal discovery).
- Findings enhance understanding of cognitive biases in causal reasoning.
- The causal discovery stage is crucial for hypothesizing causal model topology.
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