Causal inference from noise

Journal article


Climenhaga, Nevin, DesAutels, Lane and Ramsey, Grant. (2021). Causal inference from noise. Noûs. 55(1), pp. 152-170. https://doi.org/10.1111/nous.12300
AuthorsClimenhaga, Nevin, DesAutels, Lane and 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.

Year2021
JournalNoûs
Journal citation55 (1), pp. 152-170
PublisherWiley Periodicals
ISSN1468-0068
Digital Object Identifier (DOI)https://doi.org/10.1111/nous.12300
Scopus EID2-s2.0-85068619562
Open accessPublished as green open access
Page range152-170
Author's accepted manuscript
License
All rights reserved
File Access Level
Open
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online02 Jul 2019
Publication process dates
Accepted28 Apr 2019
Deposited05 Aug 2021
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