Predicting ‘It will work for us’: (Way) beyond statistics

Cartwright, Nancy

doi:10.1093/acprof:oso/9780199574131.003.0035

Cited by 15 publications

(25 citation statements)

References 2 publications

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“…8. Cartwright (2011) is another proponent of the view that successful extrapolation requires evidence that goes beyond statistical studies.…”

Section: Resultsmentioning

confidence: 99%

Establishing Causal Claims in Medicine

Williamson

2019

International Studies in the Philosophy of Science

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Russo and Williamson [2007. "Interpreting Causality in the Health Sciences." International Studies in the Philosophy of Science 21: 157-170] put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo-Williamson thesis conflicts with the tenets of present-day evidence-based medicine (EBM), it offers a better causal epistemology than that provided by present-day EBM because it better explains two key aspects of causal discovery. First, the thesis better explains the role of clinical studies in establishing causal claims. Second, it yields a better account of extrapolation. This epistemological thesis, which has become known in the literature as the Russo-Williamson thesis or RWT, has generated some controversy-see, e.g.

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“…8. Cartwright (2011) is another proponent of the view that successful extrapolation requires evidence that goes beyond statistical studies.…”

Section: Resultsmentioning

confidence: 99%

Establishing Causal Claims in Medicine

Williamson

2019

International Studies in the Philosophy of Science

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“…Nevertheless, in Cartwright (2011;, Cartwright, and Hardie (2013) and Rol and Cartwright (2012) it has been suggested that the best way to overcome this problem is to focus on the details of the population in which the intervention is intended to be implemented. Methods for hunting causes only provide information regarding a policy that worked somewhere.…”

Section: Invariance In Policy Implementation: the Nancy Cartwright's mentioning

confidence: 99%

Socioeconomic processes as open-ended results. Beyond invariance knowledge for interventionist purposes

Ivarola

2017

THEORIA

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In this paper a critique to Cartwright's approaches of capacities and nomological machines is carried out.This approaches share the idea that invariant knowledge is crucial for achieving policy purposes. However, it is shown that socioeconomic processes do not fit to the logic of stable causal factors, but they are more suited to the logic of "open-ended results". On the basis of this ontological variation it is argued that ex-ante interventions are not appropriate in the socioeconomic realm. On the contrary, they must be understood in a "dynamic" sense. Finally, derivational robustness analysis is proposed as a useful tool for overcoming the problem of "overconstraint", a typical problem of economic models.Keywords: invariance, intervention, nomological machines, capacities, derivational robustness analysis.RESUMEN: En el presente trabajo se hará una crítica a los enfoques de capacidades y máquinas nomológicas propuestos por Cartwright. Estos enfoques comparten la idea de que un conocimiento invariante es crucial para el éxito de una política. No obstante, aquí se mostrará que los procesos socioeconómicos no son compatibles con la lógica de los factores causales estables, sino con la lógica de los "resultados de final abierto". Sobre la base de esta variante ontológica se argumentará que las intervenciones ex-ante no son apropiadas en el reino de lo socioeconó-mico. Por el contrario, deberían ser entendidas en un sentido "dinámico". Finalmente, se propondrá al análisis de robustez derivacional como una herramienta útil para superar el problema de "sobre-restricción", un problema típico de los modelos económicos.Palabras clave: invarianza, intervención, máquinas nomológicas, capacidades, análisis de robustez derivacional.

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“…Organizational researchers would benefit from understanding how incorporating or shifting toward Bayesian methods will lead to different assumptions, and how Bayesian probability and methods can facilitate a capable shift when moving research findings from specific contexts to other domains-perhaps through practice (e.g., Verran, 2001). For guidance on developing "evidence for use," a welldeveloped framework is by Cartwright (2006Cartwright ( , 2007Cartwright ( , 2010.…”

Section: Empirical Management Research That Features Specific Types Omentioning

confidence: 99%

Bayesian Probability and Statistics in Management Research

Zyphur

Oswald

2012

Journal of Management

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This special issue is focused on how a Bayesian approach to estimation, inference, and reasoning in organizational research might supplement-and in some cases supplant-traditional frequentist approaches. Bayesian methods are well suited to address the increasingly complex phenomena and problems faced by 21st-century researchers and organizations, where very complex data abound and the validity of knowledge and methods are often seen as contextually driven and constructed. Traditional modeling techniques and a frequentist view of probability and method are challenged by this new reality.

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Predicting ‘It will work for us’: (Way) beyond statistics

Cited by 15 publications

References 2 publications

Establishing Causal Claims in Medicine

Establishing Causal Claims in Medicine

Socioeconomic processes as open-ended results. Beyond invariance knowledge for interventionist purposes

Bayesian Probability and Statistics in Management Research

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