Modeling our causal intuition can contribute to understanding our behavior. In this paper, we introduce a causal induction model called proportion of assumed-to-be rare instances (pARIs) and examine its adaptive properties. We employ the two-stage theory of causal induction proposed by Hattori and Oaksford in 2007, which divides causal induction into two stages: first, observed events are sifted and likely candidates are extracted; second, each of them is verified through intervention. Here, we focus on the first stage. We conducted a meta-analysis and computer simulations in a similar way to Hattori and Oaksford (2007) but with some corrections and improvements. We added two experiments and excluded one in our reconstructed meta-analysis and augmented the simulations by correcting two problems. Our meta-analysis results show that pARIs outperforms more than 40 existing models in terms of data fit from human causal induction experiments while being simpler. Additionally, our simulation results show that pARIs outperforms DFH in terms of population covariation detection, especially under small sample sizes and rarity of events. Overall, pARIs qualifies as one of the best models for the first stage of causal induction. These findings may enable a deeper understanding of our cognitive biases. The first stage can now be considered a causal discovery stage where the topology of causal models is to be hypothesized.
