David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 163 (3):409 - 418 (2008)
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.
|Keywords||Bayesian network Causal inference Consistency Error probability|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Frank Arntzenius (1992). The Common Cause Principle. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:227 - 237.
Nancy Cartwright (1999). The Dappled World: A Study of the Boundaries of Science. Cambridge University Press.
DM Hausman & J. Woodward (1999). Independence, Invariance and the Causal Markov Condition. British Journal for the Philosophy of Science 50 (4):521-583.
Michael McDermott (1995). Redundant Causation. British Journal for the Philosophy of Science 46 (4):523-544.
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Citations of this work BETA
No citations found.
Similar books and articles
Joseph Berkovitz (2002). On Causal Loops in the Quantum Realm. In T. Placek & J. Butterfield (eds.), Non-Locality and Modality. Kluwer. 235--257.
John L. Pollock (2002). Causal Probability. Synthese 132 (1-2):143 - 185.
David Papineau (1990). Causes and Mixed Probabilities. International Studies in the Philosophy of Science 4 (1):79 – 88.
Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.
Thomas Müller (2005). Probability Theory and Causation: A Branching Space-Times Analysis. British Journal for the Philosophy of Science 56 (3):487 - 520.
Frederick Eberhardt (2009). Introduction to the Epistemology of Causation. Philosophy Compass 4 (6):913-925.
Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2):239-271.
Added to index2009-01-28
Total downloads22 ( #83,019 of 1,101,884 )
Recent downloads (6 months)6 ( #52,459 of 1,101,884 )
How can I increase my downloads?