Beliefs about sparsity affect causal experimentation

Anna Coenen, Neil Bramley, Azzurra Ruggeri, Todd Gureckis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

What is the best way of figuring out which variables can cause some outcome of interest? One prominent normative proposal is that learners should manipulate each candidate variable in isolation to avoid receiving confounding information. Here, we demonstrate that this strategy is not always the most efficient method for learning about a causal system. Using an optimal learner model, 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 actually 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.
Original languageEnglish
Title of host publicationProceedings of the 39th Annual Meeting of the Cognitive Science Society
PublisherCognitive Science Society
Pages1788-1793
ISBN (Print)9780991196760
Publication statusPublished - Jul 2017
EventCogSci 2017: 39th Annual Meeting of the Cognitive Science Society - Hilton London Metropole, 225 Edgware Rd, London, United Kingdom
Duration: 26 Jul 201729 Jul 2017
http://web.archive.org/web/20180926102901/http://www.cognitivesciencesociety.org/conference/cogsci2017/

Conference

ConferenceCogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17
Internet address

Keywords / Materials (for Non-textual outputs)

  • causal learning
  • intervention
  • time
  • causal cycles
  • structure induction
  • dynamics

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  • CogSci 2017

    Neil Bramley (Presenter)

    26 Jul 201729 Jul 2017

    Activity: Participating in or organising an event typesParticipation in conference

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