Testing one or multiple: How beliefs about sparsity affect causal experimentation

Anna Coenen, Azzurra Ruggeri, Neil Bramley, Todd Gureckis

Research output: Contribution to journalArticlepeer-review

Abstract

What is the best way of discovering the underlying 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 (sometimes known as the control of variables [CV] strategy). We demonstrate that CV is not always the most efficient method for learning. Using an optimal actor model, which aims to minimize the average number of tests, we show that when a causal system is sparse (i.e., 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. Across a series of behavioral experiments, we then show that people are sensitive to causal sparsity and adapt their strategies accordingly. When interacting with a dense causal system (high proportion of actual causes among candidate variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse causal system, they are more likely to test multiple variables at once. However, we also find that people sometimes use a CV strategy even when a system is sparse.
Original languageEnglish
Pages (from-to)1923-1941
Number of pages19
JournalJournal of Experimental Psychology: Learning, Memory, and Cognition
Volume45
Issue number11
Early online date16 May 2019
DOIs
Publication statusE-pub ahead of print - 16 May 2019

Keywords

  • control of variables
  • interventions
  • experimentation
  • casual learning
  • hypothesis
  • testing

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