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“Spurious Correlations and Causal Inferences”

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Abstract

The failure to recognize a correlation as spurious can lead people to adopt strategies to bring about a specific outcome that manipulate something other than a cause of the outcome. However, in a 2008 paper appearing in the journal Analysis, Bert Leuridan, Erik Weber and Maarten Van Dyck suggest that knowledge of spurious correlations can, at least sometimes, justify adopting a strategy aiming at bringing about some change. This claim is surprising and, if true, throws into question the claim of Nancy Cartwright and others that knowledge of laws of association is insufficient for distinguishing effective and ineffective strategies. This paper examines the nature of spurious correlations and their value in crafting strategies for change. The conclusion of the paper is that while knowledge of a spurious correlation may have practical value, the value depends on either having knowledge of the causal structure underlying the correlation or it depends on the use of ‘causal criteria’.

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Notes

  1. ‘Formal education’ refers to the instruction received within an organized, institutional setting (e.g., a public school system or a University). In contrast, ‘effective education’ refers to what is in fact learned, whether that learning occurs within an organized, institutional setting or in some other fashion.

  2. Following Kevin Hoover, we can understand a causal structure, generally, as “a network of counterfactual relations that maps out the underlying mechanisms through which one thing is used to control or manipulate another.” (Hoover 2001, p. 24).

  3. One can also treat the econometric concept of a spurious correlation that results from a regression equation relating economic variables having strongly autocorrelated residuals as an example of a structural spurious correlation. (Granger and Newbold 1974, pp. 111, 113–114) However, econometricians often conclude in such cases that the relevant equation has been mis-specified (Granger and Newbold 1974, p. 117; Prather 1988), and it is this conclusion of mis-specification that differentiates it from the common cause example of a structural spurious correlation.

  4. LWD use ‘policy’ rather than ‘strategy’. However, for many people ‘policy’ has a political connotation (e.g., public health policy, business policy), whereas the meaning of ‘strategy’ is more general (in this sense, all policies are strategies, but not all strategies are policies). Thus, because LWD’s claims are not limited to political contexts, this paper uses the expression ‘strategy’ rather than ‘policy’.

  5. As previously noted, LWD use ‘policy’ rather than ‘strategy’.

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Acknowledgments

I want to thank George Maldonado and Pamela Jo Johnson for very enjoyable discussions about epidemiology, causal inferences, and spurious correlations. I also want to thank two anonymous reviewers for Erkenntnis for their insightful and extremely helpful comments on earlier versions of this paper.

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Ward, A. “Spurious Correlations and Causal Inferences”. Erkenn 78, 699–712 (2013). https://doi.org/10.1007/s10670-012-9411-6

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