  We present a novel task that measures how people generalize objects' causal
powers based on observing a single (Experiment 1) or a few (Experiment 2)
causal interactions between object pairs. We propose a computational modeling
framework that can synthesize human-like generalization patterns in our task
setting, and sheds light on how people may navigate the compositional space of
possible causal functions and categories efficiently. Our modeling framework
combines a causal function generator that makes use of agent and recipient
objects' features and relations, and a Bayesian non-parametric inference
process to govern the degree of similarity-based generalization. Our model has
a natural "resource-rational" variant that outperforms a naive Bayesian account
in describing participants, in particular reproducing a generalization-order
effect and causal asymmetry observed in our behavioral experiments. We argue
that this modeling framework provides a computationally plausible mechanism for
real world causal generalization.
