@inproceedings{3a014cf9d3044726b28926c999445cf5,
title = "Beliefs about sparsity affect causal experimentation",
abstract = "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.",
keywords = "causal learning, hypothesis testing, information search",
author = "Anna Coenen and Bramley, {Neil R.} and Azzurra Ruggeri and Gureckis, {Todd M.}",
note = "Funding Information: Acknowledgments This research was supported by NSF grant BCS-1255538 and a John S. McDonnell Foundation Scholar Award to TMG. Publisher Copyright: {\textcopyright} CogSci 2017.; 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 ; Conference date: 26-07-2017 Through 29-07-2017",
year = "2017",
language = "English (US)",
series = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition",
publisher = "The Cognitive Science Society",
pages = "1788--1793",
booktitle = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society",
}