Causal inference of ambiguous manipulations
Philosophy of Science 71 (5):833-845 (2004)
| 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. | |||||||||
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Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2).
Debra Parrish & Bridget Noonan (2009). Image Manipulation as Research Misconduct. Science and Engineering Ethics 15 (2).
Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.
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