Premium
Causal Inference from Noise
Author(s) -
Climenhaga Nevin,
DesAutels Lane,
Ramsey Grant
Publication year - 2021
Publication title -
noûs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.574
H-Index - 66
eISSN - 1468-0068
pISSN - 0029-4624
DOI - 10.1111/nous.12300
Subject(s) - causal inference , causation , causality (physics) , noise (video) , sort , causal model , statistical inference , inference , intervention (counseling) , randomized controlled trial , randomized experiment , epistemology , psychology , computer science , econometrics , cognitive psychology , artificial intelligence , mathematics , statistics , medicine , philosophy , information retrieval , physics , surgery , quantum mechanics , psychiatry , image (mathematics)
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.