Causal identifiability and piecemeal experimentation

Causal identifiability and piecemeal experimentation Synthese https://doi.org/10.1007/s11229-018-1826-4 S.I.: EVIDENCE AMALGAMATION IN THE SCIENCES Causal identifiability and piecemeal experimentation Conor Mayo-Wilson Received: 18 May 2017 / Accepted: 21 May 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract In medicine and the social sciences, researchers often measure only a hand- ful of variables simultaneously. The underlying assumption behind this methodology is that combining the results of dozens of smaller studies can, in principle, yield as much information as one large study, in which dozens of variables are measured simultane- ously. Mayo-Wilson (Philos Sci 78(5):864–874, 2011, Br J Philos Sci 65(2):213–249, 2013. https://doi.org/10.1093/bjps/axs030) shows that assumption is false when causal theories are inferred from observational data. This paper extends Mayo-Wilson’s results to cases in which experimental data is available. I prove several new theorems that show that, as the number of variables under investigation grows, experiments do not improve, in the worst-case, one’s ability to identify the true causal model if one can measure only a few variables at a time. However, stronger statistical assumptions (e.g., Gaussianity) significantly aid causal discovery in piecemeal inquiry, even if such assumptions are unhelpful when all variables can be measured simultaneously. Keywords Causation · Experimentation · Induction http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Synthese Springer Journals

Causal identifiability and piecemeal experimentation

Synthese , Volume OnlineFirst – May 28, 2018

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Philosophy; Philosophy of Science; Epistemology; Logic; Philosophy of Language; Metaphysics
ISSN
0039-7857
eISSN
1573-0964
D.O.I.
10.1007/s11229-018-1826-4
Publisher site
See Article on Publisher Site

Abstract

Synthese https://doi.org/10.1007/s11229-018-1826-4 S.I.: EVIDENCE AMALGAMATION IN THE SCIENCES Causal identifiability and piecemeal experimentation Conor Mayo-Wilson Received: 18 May 2017 / Accepted: 21 May 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract In medicine and the social sciences, researchers often measure only a hand- ful of variables simultaneously. The underlying assumption behind this methodology is that combining the results of dozens of smaller studies can, in principle, yield as much information as one large study, in which dozens of variables are measured simultane- ously. Mayo-Wilson (Philos Sci 78(5):864–874, 2011, Br J Philos Sci 65(2):213–249, 2013. https://doi.org/10.1093/bjps/axs030) shows that assumption is false when causal theories are inferred from observational data. This paper extends Mayo-Wilson’s results to cases in which experimental data is available. I prove several new theorems that show that, as the number of variables under investigation grows, experiments do not improve, in the worst-case, one’s ability to identify the true causal model if one can measure only a few variables at a time. However, stronger statistical assumptions (e.g., Gaussianity) significantly aid causal discovery in piecemeal inquiry, even if such assumptions are unhelpful when all variables can be measured simultaneously. Keywords Causation · Experimentation · Induction

Journal

SyntheseSpringer Journals

Published: May 28, 2018

References

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