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Abstract
In the causal inference framework of Spirtes, Glymour, and Scheines (SGS), inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper we show that the addition of the Causal Faithfulness Assumption plays three quite different roles in the SGS framework: (i) it reduces the degree of underdetermination of causal structure by probability distribution; (ii) computationally, it justifies reliable (constraintbased) causal inference algorithms that would otherwise have to be slower in order to be reliable; and (iii) statistically, it implies that those algorithms reliably obtain the correct answer at smaller sample sizes than would otherwise be the case. We also consider a number of variations on the Causal Faithfulness Assumption, and show how they affect each of these three roles.
Original language  English 

Pages (fromto)  10111027 
Number of pages  17 
Journal  Synthese 
Volume  193 
Issue number  4 
Early online date  11 Feb 2015 
DOIs  
Publication status  Published  Apr 2016 
Keywords
 Bayes nets
 Causal inference
 Faithfulness
 Graphical models
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Projects
 1 Finished

Philosophical Implications of Recent Advances in Causal Modeling (因果建模和推理的新方法的哲學探究)
ZHANG, J. & ZHANG, K.
Research Grants Council (HKSAR)
1/08/13 → 31/01/16
Project: Grant Research