Proceedings of the Annual Meeting of the Cognitive Science Society

What is the best way of figuring out the structure of a causalsystem composed of multiple variables? One prominent ideais that learners should manipulate each candidate variable inisolation to avoid confounds (known as the “Control of Vari-ables” strategy). Here, we demonstrate that this strategy is notalways the most efficient method for learning. Using an opti-mal learner model which aims to minimize the number of tests,we show that when a causal system is sparse, that is, whenthe outcome of interest has few or even just one actual causeamong the candidate variables, it is more efficient to test mul-tiple variables at once. In a series of behavioral experiments,we then show that people are sensitive to causal sparsity whenplanning causal experiments.