Title
Error probabilities for inference of causal directions
Document Type
Journal article
Source Publication
Synthese
Publication Date
2008
Volume
163
Issue
3
First Page
409
Last Page
418
Keywords
Bayesian network; Causal inference; Consistency; Error probability
Abstract
A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods.
DOI
10.1007/s11229-007-9295-1
Print ISSN
00397857
E-ISSN
15730964
Publisher Statement
Copyright © 2008 Springer Netherlands.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Zhang, J. (2008). Error probabilities for inference of causal directions. Synthese, 163(3), 409-418. doi: 10.1007/s11229-007-9295-1
