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
Synthese – Springer Journals
Published: May 28, 2018
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