Hostname: page-component-7479d7b7d-wxhwt Total loading time: 0 Render date: 2024-07-14T15:11:35.050Z Has data issue: false hasContentIssue false

Are RCTs the Gold Standard?

Published online by Cambridge University Press:  01 March 2007

Nancy Cartwright
Affiliation:
Department of Philosophy, Logic and Scientific Method, London School of Economics, Houghton Street, London WC2A 2AE, UK E-mail: n.l.cartwright@lse.ac.uk
Get access

Abstract

The claims of randomized controlled trials (RCTs) to be the gold standard rest on the fact that the ideal RCT is a deductive method: if the assumptions of the test are met, a positive result implies the appropriate causal conclusion. This is a feature that RCTs share with a variety of other methods, which thus have equal claim to being a gold standard. This article describes some of these other deductive methods and also some useful non-deductive methods, including the hypothetico-deductive method. It argues that with all deductive methods, the benefit that the conclusions follow deductively in the ideal case comes with a great cost: narrowness of scope. This is an instance of the familiar trade-off between internal and external validity. RCTs have high internal validity but the formal methodology puts severe constraints on the assumptions a target population must meet to justify exporting a conclusion from the test population to the target. The article reviews one such set of assumptions to show the kind of knowledge required. The overall conclusion is that to draw causal inferences about a target population, which method is best depends case-by-case on what background knowledge we have or can come to obtain. There is no gold standard.

Type
Articles
Copyright
Copyright © London School of Economics and Political Science 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Clarendon Press.Google Scholar
Cartwright, N. (forthcoming). Hunting causes and using them. Cambridge: Cambridge University Press.Google Scholar
Granger, C. (1969). Investigating causal relations by econometric models and cross-special methods. Econometrica, 37, 424438.CrossRefGoogle Scholar
Heckman, J. (2001). Econometrics, counterfactuals and causal models. Keynote Address, International Statistical Institute, Seoul, Korea.Google Scholar
Holland, P.W., & Rubin, D.B. (1988). Causal inference in retrospective studies. Evaluation Review, 12, 203231.CrossRefGoogle Scholar
Suppes, P. (1970). Probabilistic theory of causality. Atlantic Highlands, NJ: Humanities Press.Google Scholar