Beliefs about sparsity affect causal experimentation

What is the best way of figuring out the structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (known as the “Control of Variables” strategy). Here, we demonstrate that this strategy is not always the most efficient method for learning. Using an optimal learner model which aims to minimize the number of tests, we show that when a causal system is sparse, that is, when the outcome of interest has few or even just one actual cause among the candidate variables, it is more efficient to test multiple variables at once. In a series of behavioral experiments, we then show that people are sensitive to causal sparsity when planning causal experiments.