  Higher-level cognition depends on the ability to learn models of the world.
We can characterize this at the computational level as a structure-learning
problem with the goal of best identifying the prevailing causal relationships
among a set of relata. However, the computational cost of performing exact
Bayesian inference over causal models grows rapidly as the number of relata
increases. This implies that the cognitive processes underlying causal learning
must be substantially approximate. A powerful class of approximations that
focuses on the sequential absorption of successive inputs is captured by the
Neurath's ship metaphor in philosophy of science, where theory change is cast
as a stochastic and gradual process shaped as much by people's limited
willingness to abandon their current theory when considering alternatives as by
the ground truth they hope to approach. Inspired by this metaphor and by
algorithms for approximating Bayesian inference in machine learning, we propose
an algorithmic-level model of causal structure learning under which learners
represent only a single global hypothesis that they update locally as they
gather evidence. We propose a related scheme for understanding how, under these
limitations, learners choose informative interventions that manipulate the
causal system to help elucidate its workings. We find support for our approach
in the analysis of four experiments.
