Author: causehealthnmbu

Cause Health - Causation, Complexity and Evidence in Health Sciences


by Elena Rocca


In the early 19th century, the Hungarian physician Ignaz Semmelweis noticed from his clinical experience that antiseptic routines in healthcare reduced infections at childbirth. After carrying out some studies on the matter, he proposed that the practice of disinfecting hands in the obstetrician ward of the Vienna General Hospital, where he worked at the time, would have reduced the incidence of puerperal fever. However, for that time this seemed as an implausible suggestion.  The germ theory of disease was still unheard of (Pasteur developed such theory only some decades later), and therefore there was no accepted understanding of how disease could be transmitted from one organism to the other. Semmelweis suggestion was therefore rejected by the medical community.

This historical anecdote is often quoted as a reminder that background knowledge and theoretical understanding of causal mechanisms can be at any time wrong and incomplete, and therefore it can hinder the correct causal inference. How to amend this fact, in modern medical research and practice?

The mainstream strategy comes from the evidence-based medicine (EBM) proponents. Since when we try to understand causation in medicine we risk to run into a lot of troubles, we should improve our ability of looking at correlation data without trying to understand phenomena, or causal mechanisms, underlying such correlations. What when statistical studies give conflicting results? In this case, we should trust the most unbiased experimental design. In other words, we are better off if we focus on judging the quality of the methods used to collect and analyse statistical data, and drop the attempts to understand infinitely complex biological phenomena underlying such data.

In a new CauseHealth paper, ‘The Judgements That Evidence-Based Medicine Adopts’, Elena Rocca objects this strategy by arguing that it is impossible to apply when complex evidence needs to be weighted. When different experimental designs yield conflicting results, we necessarily adopt our background, theoretical understanding of phenomena and causal mechanisms in order to judge which study is less biased. For instance, we need such background understanding to judge whether a trial is successfully randomised. The evaluation of any type of evidence, argues the paper, is based not only on that specific evidence that is being evaluated, but also on background knowledge. This is built by more general, previously accumulated evidence and by theoretical understanding of phenomena.


The paper demonstrates this thesis by looking at complex cases in which conflicting statistical evidence had to be evaluated, for instance the case of correlation between the exposure to the herbicide Glyphosate with higher incidence of lymphoma.

Clearly, background knowledge can be wrong and incomplete. When explanations are wrong, they will probably hinder, rather than favour, the correct causal evaluation. However, as this article attempts to demonstrate, such explanations are irreducibly embedded in the medical sciences. This fallibility, concludes the author, is therefore ‘a motivation for increasing our enquiry on causal explanations, rather than for dismissing it’.


CauseHealth goes to Evidence Live


Evidence Live is an annual conference, jointly hosted by the Centre for Evidence-Based Medicine, University of Oxford and The BMJ. This year, CauseHealth was represented in two of the sessions, by Elena Rocca and Rani Lill Anjum. (more…)

Evidence based medicine. What evidence, whose medicine, and on what basis?


Rani Lill Anjum

The evidence-based medicine movement was intended as a methodological revolution. Its proponents suggested the best way to establish the effectiveness of treatment and new criteria to choose between available treatments without bias. Philosophically, however, these changes were not so innocent, at least not ontologically speaking. In bringing itself closer to science, medicine has become less suitable for dealing with complex illnesses, individual variations and, as I will argue, with causation in general. (more…)

Capturing the Colour: Classification and its Consequences

Author Eivind Hasvik
(#5 in the Whole Person reflections series)

Gazing through my window, I’m enriched by a muted but beautiful December twilight-palette. The remains of autumn covered by a thin layer of snow. It’s said that every culture has its own sense of the different hues. I’m reading a beautiful passage in White by Kenya Hara about the traditional Japanese way of naming colours. Contrary to the modern way of categorizing a given spectrum of light, such as greens, magentas or yellows, it’s said that red, blue, white and black were the only basic colour adjectives in 8th century Japan. The tradition was not to classify, but to describe and texturize, capturing the seasons and surroundings. This narrative heritage is beautifully documented in the book The traditional colours of Japan.

I’m imagining a metaphorical link from all this to the difficulties of describing experience—sensations, emotions, pain or pleasure. (more…)

Does your regular GP know you – as a person? And if so, does it matter?

Written by Bente Prytz Mjølstad
(#3 of the Whole Person reflections series)

Have you ever thought about whether your regular GP knows more about you than your blood pressure or cholesterol levels? If so, might such knowledge be of any medical relevance?

Most of us visit our regular GP once or twice a year for more or less trivial complaints, and you are probably most interested in the GPs medical skills, and not so concerned about whether the doctor knows you as person or not. However, if you got seriously ill or had a chronic illness, would it still not matter? (more…)

CauseHealth workshop N=1 is now a section in JECP special issue.


The Journal of Evaluation in Clinical Practice has dedicated a section of its latest special issue to collect seven contributions which were previously presented in the CauseHealth workshop N=1. A further contribution from the same workshop was published by the same journal last year. (more…)