This was the theme of a symposium at Oxford yesterday, organised by Jeremy Howick. We covered a broad range of answers to this question, including: nothing (Ray Tallis), understanding the role of values (Elselijn Kingma), linguistic analysis (Bill Fulford), training for medical students (Alexander Bird).
My line of argument was that the recent 15 has much to offer the evaluation of causal claims.
Since 2000, philosophers of science have devoted a lot of attention to mechanisms. As Machamer, Darden and Craver (2000: Philosophy of Science 67:1–25) point out:
In many fields of science what is taken to be a satisfactory explanation requires providing a description of a mechanism. So it is not surprising that much of the practice of science can be understood in terms of the discovery and description of mechanisms.
Philosophers have naturally been interested in such questions as: What are mechanisms? What are mechanistic explanations? How do mechanisms relate to causal connections?
On this last question, one can make a strong case for the following thesis:
In medicine, in order to establish that A is a cause of B we typically need to establish:
Association. A and B are correlated in the appropriate way.
Mechanism. A mechanistic connection between them is responsible for this correlation.
Association is not enough on its own, because the correlation might be due to:
- Sampling. A statistical blip.
- Confounding. Each variable is correlated with an unobserved common cause.
- Time Series. Drift in each variable over time.
- Semantic Connection. The variables have overlapping meaning.
- Logical Connection. Particularly between logically complex variables.
- Physical Connection. E.g., in virtue of the law of conservation of total energy.
- Mathematical Connection. E.g., mean and variance variables defined relative to the same distribution.
One needs to rule out these alternative explanations of an observed correlation if one is to infer that the association is causal. This means determining two things:
- There exists some such mechanism that can explain the correlation.
- This explanation is the best explanation of the correlation.
Current EBM hierarchies of evidence address the question of Association, but not that of Mechanism. The rankings they advocate may well be appropriate if one wants to determine whether there is a correlation between A and B, but they are not suitable for evaluating evidence of mechanisms. This is because, while RCTs offer perhaps the best route to evidence of association, one can get high-quality evidence of mechanisms from a wide variety of sources:
- Direct manipulation: e.g., in vitro experiments
- Direct observation: e.g., biomedical imaging, autopsy, case reports
- Statistical trials: e.g., RCTs
- Confirmed theory: e.g., literature searches
- Analogy: e.g., animal experiments
- Simulation: e.g., agent-based models
So, philosophy of science has something useful to say about EBM:
- Association is only half the story.
- In order to establish causality, we also need to establish Mechanism. It is here that a thorough understanding of evidence of mechanisms is invaluable.
The mechanistic turn in the philosophy of science holds much promise, I think.
Further reading: Clarke et al. (2013); Russo & Williamson (2007).