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Experimentation and Causal Reasoning

Started by minuspace, February 18, 2012, 11:12:34 PM

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"Causal reasoning approaches try to reconstruct and sometimes justify the rules that allow scientists to infer causal relationships from data, including experimental data....

Mill's method of difference captures an important kind of reasoning that is used frequently in biological experiments. ..

Mill construed this "method" in terms of a principle of inductive inference that can be justified pragmatically. However, it is interesting to note that the principle can also be viewed as instantiating a form of deductive inference.

To this end, of course, the Method of Difference must be strengthened with additional premises. Here is one way how this can be done (adapted from Hofmann and Baumgartner 2011):

S1 and S2 are two homogeneous test situations (assumption)
Two factors A and W both occur in S1 both not in S2 (exp. result)
W is an effect in a deterministic causal structure (assumption)
In S1 there exists a cause of the occurrence of W (from 2, 3)
In S2 there exists no cause of the occurrence of W (from 2,3)
S2 contains no confounder of W (from 5)
S1 contains no confounder of W (from 1,6)
The cause of W belongs to the set {A, W} (from 4, 7)
W does not cause itself (assumption)
A is the cause or a part of the cause existing in S1


I don't know why, but I think there may be something interesting about this?  (or maybe later). Just a random thought, really, probably nothing...