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Causal Inference from Noise

Climenhaga, Nevin and DesAutels, Lane and Ramsey, Grant (2019) Causal Inference from Noise. [Preprint]

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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.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Climenhaga, Nevinnevin.climenhaga@acu.edu.au0000-0002-7376-8788
DesAutels, Lanelane.desautels@gmail.com
Ramsey, Grantgrant@theramseylab.org0000-0002-8712-5521
Additional Information: Forthcoming in Noûs
Keywords: Causal Inference, Randomized Controlled Trials, Noise, Probability
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Medicine > Clinical Trials
General Issues > Confirmation/Induction
Specific Sciences > Medicine > Epidemiology
General Issues > Experimentation
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
Depositing User: Nevin Climenhaga
Date Deposited: 08 May 2019 01:46
Last Modified: 08 May 2019 01:46
Item ID: 15984
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Medicine > Clinical Trials
General Issues > Confirmation/Induction
Specific Sciences > Medicine > Epidemiology
General Issues > Experimentation
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
Date: May 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/15984

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