Testing one or multiple: How beliefs about sparsity affect causal experimentation.
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 (known as the ``Control of Variables'' or CV strategy). Here, we demonstrate that CV is not always the most efficient method for learning. Using an optimal learner model which aims to minimize the average number of tests, we show that when a causal system is sparse (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. When interacting with a non-sparse causal system system (high proportion of causes among variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse system (low proportion of causes among variables) they are more likely to test multiple variables at once. However, we also consistently find that some people use a CV strategy even when a system is sparse. This runs counter to prior work, which showed that the CV principle can be challenging to learn.
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Bibtex entry:
@article{coenen2019testing,
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 (known as the ``Control of Variables'' or CV strategy). Here, we demonstrate that CV is not always the most efficient method for learning. Using an optimal learner model which aims to minimize the average number of tests, we show that when a causal system is sparse (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. When interacting with a non-sparse causal system system (high proportion of causes among variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse system (low proportion of causes among variables) they are more likely to test multiple variables at once. However, we also consistently find that some people use a CV strategy even when a system is sparse. This runs counter to prior work, which showed that the CV principle can be challenging to learn.},
author = {Coenen, A. and Ruggeri, A. and Bramley, N.R. and Gureckis, T.M.},
awards = {Student Coenen awarded 2019 Early Career Award, Society for Experimental Psychology and Cognitive Science - SEPCS, Div 3 of APA for this paper},
journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
number = {11},
pages = {1923},
publisher = {American Psychological Association},
title = {Testing one or multiple: How beliefs about sparsity affect causal experimentation.},
volume = {45},
year = {2019}}QR Code:
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