Herein we describe CRAMM, a framework for Causal Reason-ing via Attention and Mental Models. CRAMM develops andextends assumptions made by a previously developed coun-terfactual simulation model of human causal judgment. Weimplement CRAMM computationally and demonstrate how itrobustly captures human causal judgments about simple two-object interactions at the level of underlying cognitive and per-ceptual processes, including data on eye-movements that serveas direct evidence for the role of counterfactuals in causal judg-ment.
