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Causal Inference of Ambiguous Manipulations

Published online by Cambridge University Press:  01 January 2022

Abstract

Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, although the manipulator does not know that this is the case. In this article we revisit the question of precisely characterizing conditions and assumptions under which reliable inference about the effects of manipulations is possible, even when the possibility of “ambiguous manipulations” is allowed.

Type
Causation and Bayesian Networks
Copyright
Copyright © 2004 by the Philosophy of Science Association

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Footnotes

We thank Clark Glymour for valuable discussions and comments.

References

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