• Anjum, R.L., Mumford, S., Dispositional Modality (2011)
• Anjum, R.L., Causation in Scientific Methods (2016)
• Landes, J. et at, Epistemology of Causal Inference in Pharmacology: Towards a Framework for the Assessment of Harms (2016)
• Mumford, S. Causal Powers and Capacities (2010)
• Mumford, S., Lill Anjum, R., A Powerful Theory of Causation (2010)
• Mumford, S.; Lill Anjum, R, Getting Causes from Powers (2011)
• Mumford, S, Anjum, R.L., Causation in Science (forthcoming) (abstracts available)
• Journal of Evaluation in Clinical Practice, Special Issue: 2017, Philosophy Thematic Issue, October 2017, Volume 23, Issue 5, Pages 903–1131
  • Reasoning, evidence, and clinical decision-making: The great debate moves forward (pages 905–914) PDF
  • Managing uncertainty in diagnostic practice (pages 959–963), Ashley Graham Kennedy
  • A philosophical argument against evidence-based policy (pages 1045–1050), Rani Lill Anjum and Stephen D Mumford

Landes, J. et at, Epistemology of Causal Inference in Pharmacology: Towards a Framework for the Assessment of Harms (2016)

El proyecto Ciencia en el Parlamento busca “promover una cultura de formulación de políticas basada en la evidencia (científica)”.

¿Cómo afecta la cuestión de qué es “evidencia” al tipo de conocimientos que pueden pasar a la legislación como “verdades”?

Interdisciplinary research project CauSci - Causation in Science - project ended in 2015.

• https://en.wikipedia.org/wiki/Causation_in_Sciences_Project
• https://twitter.com/CauSci
• Mumford, S, Anjum, R.L., Causation in Science (forthcoming) (abstracts available)

Research project at Norwegian University of Life Sciences. CAPS: Causation, Complexity and Evidence in Health Sciences.

• https://twitter.com/Cause_Health
• https://raniblogsaboutcausation.wordpress.com/
• http://www.sorites.org/Issue_19/anjum.htm
• https://sites.google.com/site/stephendmumford/home/unpublished-work

Blog

• Capturing the Colour: Classification and its Consequences. In this blog post, @blaatimen shares his thought about asking people to describe their pain on a 1-10 scale.

The idea of the conference was to discuss some challenges facing anyone developing and implementing clinical guidelines in the evidence based era of medicine. Some challenges relate to philosophical foundations of medicine:

• How to study and understand causal complexity if causes must be established one by one, or in isolation?
• How to deal with large individual variations if the same cause is supposed to give the same effect, under some normal or ideal conditions?
• How to make causal decisions about an individual case if the causal evidence is largely statistical?
• How to understand illness as belonging to the whole person if this whole is studied through fragmentation; part-by-part?
• What is The Guidelines Challenge?
• Canal en YouTube con vídeos de las ponencias

La revista online The Reasoner contiene una sección en cada número denominada Evidence-based medicine dirigida por Michael Wilde.

John Williamson, editor y fundador de la revista The Reasoner, investiga la denominada Systems Medicine y ha escrito en febrero de 2017 un paper titulado Models in Systems Medicine.

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally.

Systems medicine applies systems approaches, analogous to those used in systems biology, with the aim of improving medical treatment and progressing medical science. These approaches are often described as ‘data-intensive’ or ‘data-driven’ because they attempt to draw inferences from a variety of large datasets. This paper explores two problems that face systems medicine. First, there is the problem of diversity of evidence: in addition to large amounts of data (‘big data’), the available evidence tends also to be very heterogeneous, and the question arises as to how the whole range of evidence can be integrated in a coherent manner, to enable reliable inferences. The second problem is that of diversity of models: systems medicine employs different models for different purposes, and it is often far from clear as to how these models relate to one another. Can anything be done to shed light on the relationships between models?

This paper develops a normative response to these problems. It puts forward an approach based on Bayesian epistemology for integrating multiple datasets. It then puts forward a way to integrate evidence of mechanisms, which can often be qualitative, into the resulting quantitative models. (This approach can be thought of as a contribution to the EBM+ programme, which seeks ways of integrating evidence of mechanisms with evidence of associations in order to lead to better outcomes in medicine—see ebmplus.org.) The paper goes on to suggest that Bayesian networks can provide a unified modelling formalism. (This conclusion, if not the detail of the approach, is in line with that of Landes et al. (2017), who present a Bayesian network modelling framework for inference in pharmacology.) There is no claim that the framework developed here is the only way to tackle the foundational problems that face systems medicine, but it is hoped that the present attempt will encourage others to tackle these problems.

The paper is structured as follows.

1. §1 introduces systems medicine and notes that its appeal to a wide variety of data makes it a promising new paradigm for medical research. However, progress in systems medicine has not been as rapid as some have anticipated.
2. In §2 it is suggested that this slow progress might be explained by the enormity of the challenges faced by systems medicine. Two challenges stand out as particularly pressing: how should the massive amount of evidence in systems medicine be integrated? how should one go about modelling in systems medicine?
3. In §3 I classify models in systems medicine as being of four kinds: quantitative models of association; quantitative causal models; qualitative mechanistic models; and quantitative mechanistic models. In §4 I show how objective Bayesian epistemology can be applied to data integration and how an objective Bayesian net can be used as an association model. In §5 I then sketch a principled way of generating a causal model, and of structuring the development of models in systems medicine in general.