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Causal Inference of Ambiguous Manipulations

Published online by Cambridge University Press:  01 January 2022

Peter Spirtes  and
Richard Scheines
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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
Information
Philosophy of Science , Volume 71 , Issue 5: Proceedings of the 2002 Biennial Meeting of The Philosophy of Science Association. Part II: Symposia Papers , December 2004 , pp. 833 - 845
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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Spirtes, Peter, and Scheines, Richard (2003), Causal Inference of Ambiguous Manipulations. Technical Report 138, Department of Philosophy, Pittsburgh: Carnegie Mellon University.Google Scholar