On the topic of the relationship between causation and inference (which was the subject of my previous blog), please see Christian Borgelt & Rudolf Kruse, Probabilistic Networks and Inferred Causation, 18 Cardozo Law Review 2001 (1997).
You need the hard copy version of this article -- a LEXIS or WESTLAW version will not do -- because you need to see the authors' diagrams.
If you are not familiar with graph theory -- and even if you are --, you might want to skim (the more difficult) portions of Part I of the article. But plow ahead! Fear not! With a bit of effort, you can get the drift of the rest of the argument. And you will reap rewards from your labor -- and, to be sure, from your labour.
The main point of the article is a critique -- a gentle critique -- of a proposal by Judea Pearl for inferring causation from correlation, associations, or observed regularities. This critique is in itself very interesting, illuminating, and suggestive: it raises fundamental questions about the structure of causation or causal influence. But even if that critique does not interest you or if the phrasing of the athors' critique eludes your comprehension, read on!
While (gently) questioning a particular approach to the problem of inferring causation, the authors remain largely convinced -- but with qualifications and hedges -- , the authors remained convinced of the importance of causal explanations for inference, and they serve up some familiar but very useful reminders of the perils of drawing conclusions on the basis of correlation or association alone. (I found it particularly reassuring to be reminded that "causal correlations are fairly rare." [I found this assertion reassuring because I agree with it: the notion that experience alone is "voiceless" plays a large part in my thinking about inference.])
I am personally ontologically attracted -- what awful language, eh?! --, I am attracted to the thesis that in many or most domains of human life something like a principle of causation holds, that prior events do, in some way, generally influence subsequent events in the space-time continuum in which we seem to exist. I am also very strongly attracted to the hypothesis that the power of inference is greatly enhanced when human actors have and use plausible accounts of the mechanisms or processes that underlie "surface" phenomena, events, and associations. (One of my favorite cliches: experience does not speak for itself.)
But there is a great practical difficulty (and, in an important sense, a theoretical one -- because the absence of human omniscience must figure large in any "theoretical" analysis of inference), -- there is, let me simply say, a VERY BIG difficulty: In many situations human comprehension of "causes" is extraordinarily frail. In these situations, how is inference to work (ideally, but for real human beings)?
Even when human knowledge of underlying causes or mechanisms is limited, it remains true that experience and associations do not speak for themselves. The world is replete with spurious and misleading associations! So some sort of sense of how the world works perhaps -- very probably -- remains important for the drawing of sound inferences from observed or reported regularities in the world.
So, Gentle Reader, where does all of this leave us -- and, for example, where does it leave jurors or where should it leave them when they turn to the job of assessing evidence of, say, the past criminal behavior of a defendant on trial for a crime or the defendant's habit of "associating with criminals"? What, if anything, should we tell jurors or what evidence, if any, should be withhold from their gaze to make sure that they do not make inferential mistakes?
Your thoughts, Gentle Reader? (Don't be bashful! ... O.k., o.k., not-so-gentle readers can chime in too.)