Ray,
Regarding your request for examples of causal theorizing in medicine, I
would point to:
Prystowsky EN, and Fry ET. “Atrial Fibrillation and Incident Myocardial
Infarction.” JAMA 312, no. 10 (September 10, 2014): 1049–50.
doi:10.1001/jama.2014.5742.
I can’t recall the last time I saw so much theory-building and -testing
concentrated on a single page of a medical journal.
(Patricia, thank you for your interesting question and links therein.)
Regards,
David
--
David C. Norris, MD
David Norris Consulting, LLC
Seattle, WA
[log in to unmask]
On 11/12/14, 11:08 AM, "Raymond Pawson" <[log in to unmask]> wrote:
>Hi Patricia (and all)
>
>Now this is really important. Real causal inferences in science are made
>in the manner you suggest. Theories are built, contested and refined over
>time using a plethora of different empirical tests – none of them
>decisive in isolation. This is well described by the philosophers of
>science – Popper, Lakatos, Campbell (the real one) etc. Hill’s criteria
>capture the balance of many considerations that contribute to strong
>causal explanations and remain influential in the public health
>community. None of these sources translate simply into programme
>evaluation.
>
>The great irony is that these ‘conjectures are refutations’ models
>describe perfectly the many different empirical tests that are applied in
>the lengthy process of developing clinical interventions. These start in
>basic science, pre-clinical work hypothesising the underlying disease
>pathology and potential mechanisms of action that might target the
>particular condition. Then there is a phase of therapeutic discovery in
>which compounds and techniques are tried, refined in an attempt to embody
>the conjectured mechanisms and then laboratory tested the see if the
>initial explanation holds promise. Then, still in pre-clinical phases,
>there are tests, for instance, about the absorption, distribution,
>metabolism and excretion of a drug. Only then we get to effectiveness
>with patients and the three phases of clinical trials, starting with
>proof of concept, dose-finding and safety studies, moving eventually to
>RCTs and regulatory proof. There is even a further stage about long-term
>follow-up and the detection of ‘rare events’. Failure is commonplace at
>any stage – in which case the hypotheses are refined and the
>hypotheses-testing cycle is resumed.
>
>Here’s the problem. A) Cochranites have managed to convince themselves
>(and much of the medical establishment) that only the penultimate stage
>counts in assessing causality and effectiveness. B) Even those keenly
>attuned to the full cycle explanation often don’t like to use the
>language of ‘theory’ testing (they often prefer rather technical or
>mechanical accounts of each individual stage).
>
>So you are right about the desperate need for good examples of
>non-counterfactual causal explanations. Finding them in medicine would be
>the ultimate coup de grace. Rather late in the day I’m trying to become
>an amateur medic in trying to piece together the full story as above. Any
>good sources graciously welcomed!
>
>Thanks by the way for the links to Julian and Jane’s excellent work.
>
>RAY
>________________________________________
>From: Realist and Meta-narrative Evidence Synthesis: Evolving Standards
>[[log in to unmask]] On Behalf Of Patricia Rogers
>[[log in to unmask]]
>Sent: Tuesday, November 11, 2014 9:36 PM
>To: [log in to unmask]
>Subject: Example of causal inference in evaluation without a
>counterfactual
>
>Dear RAMESES colleagues,
>
>After such great suggestions of examples of realist evaluations, I'd like
>to make a related request.
>
>Can you suggest any good examples of an impact evaluation that uses
>non-counterfactual causal inference - that is, doing lots of small tests
>that the data fit the theory of a causal relationship, and ruling out
>alternative explanations?
>
>While there's good material that discusses these strategies (eg Bradford
>Hill's classic 1965 paper,<http://www.edwardtufte.com/tufte/hill> on
>Edward Tufte's site, Julian King's new
>e-book<http://www.julianking.co.nz/wp-content/uploads/2014/08/140826-BHC-w
>eb.pdf> on the Bradford Hill criteria. and Jane Davidson's
>webinar<http://betterevaluation.org/events/coffee_break_webinars_2013#webi
>narPart5> on causal inference) I struggle to find good examples that can
>be used in a workshop to clearly demonstrate the logic of
>non-counterfactual causal inference, can be readily understood and are
>clearly relevant.
>
>The old epidemiological examples of John Snow's investigation of cholera
>in London and the link between lung cancer and smoking explain the logic
>but are too often dismissed as being not relevant because they are "the
>cause of an effect, not the effect of a cause" - as in an impact
>evaluation of a known program. (I think this is a specious argument but
>that's a hard position to change).
>
>I'd really appreciate suggestions of good examples I can add to the
>BetterEvaluation site <http://betterevaluation.org/plan/understandcauses>
>and share in workshops and presentations.
>
>Patricia Rogers
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