By Mikel Aickin
''Provides present types, instruments, and examples for the formula and assessment of clinical hypotheses in causal phrases. Introduces a brand new approach to version parametritization. Illustrates structural equations and graphical parts for complicated causal systems.''
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Extra resources for Causal Analysis in Biomedicine and Epidemiology: Based on Minimal Sufficient Causation
As I indicated in the previous section, I view causation as a relationship between events. The first step down this path is to define the collection Q of all opportunities for events to occur. A typical element of Q. is written co. Q and GO are the Greek capital and lower case omega, which we can identify with the word "opportunity". An event is nothing more than a subset of Q. The elements of the set are the opportunities at which the event "happened", and those outside the set are those opportunities where it didn't.
But there is a problem here. What about the case in which AB o ABC? That is, if AB => C, then the statement that AB occurred is exactly the same as saying that ABC occurred; the addition of the C term has a notational effect (AB is not the same set of symbols as ABC), but no actual effect in terms of the underlying events. 17 3. Naive Minimal Sufficient Cause To be clear, suppose that G is some additional event that has nothing to do with D. Let G* be the complement of G (that is, the event consisting of all non-occurrences of G).
The problem that arises is that if the F I select is too small, then C[y|F] may only account for a very small proportion of y events. In other words, although the above causal equation is correct, it may not say anything about most occurrences of y. There is a way to fix this. i and b1; and so if I could observe every element of F then of course I could observe these, too. Suppose I were to imagine that, to the contrary, F contained factors that I could not measure. There are plenty of practical examples when this is the case, and in point of fact the case where all factors are observable is the highly unusual one.