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Causal inference from noise
Climenhaga, Nevin DesAutels, Lane Ramsey, Grant
Climenhaga, Nevin
DesAutels, Lane
Ramsey, Grant
Abstract
Correlation is not causation is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data.
Keywords
Date
2021
Type
Journal article
Journal
Noûs
Book
Volume
55
Issue
1
Page Range
152-170
Article Number
ACU Department
Institute for Religion and Critical Inquiry
Faculty of Theology and Philosophy
Faculty of Theology and Philosophy
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Relation URI
Source URL
Event URL
Open Access Status
Published as green open access
License
File Access
Controlled
Open
Open