Geometry of the faithfulness assumption in causal inference
نویسندگان
چکیده
منابع مشابه
Geometry of faithfulness assumption in causal inference
Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main justification for imposing this assumption is that the set of unfaithful distributions has Lebesgue measure zero, since it can be seen as a collection of hypersurfaces in a hypercube. However, due to sampling error the faithfulness condition alone is not sufficient for statistical estimation, and stron...
متن کاملGeometry of the Faithfulness Assumption in Causal Inference
Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main justification for imposing this assumption is that the set of unfaithful distributions has Lebesgue measure zero, since it can be seen as a collection of hypersurfaces in a hypercube. However, due to sampling error the faithfulness condition alone is not sufficient for statistical estimation, and stron...
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Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “OrientationFaithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the ...
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A fundamental question in causal inference is whether it is possible to reliably infer ma nipulation effects from observational data. There are a variety of senses of asymptotic reliability in the statistical literature, among which the most commonly discussed frequen tist notions are pointwise consistency and uniform consistency (see, e.g. Bickel, Dok sum [200 1]). Uniform consistency is in...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2013
ISSN: 0090-5364
DOI: 10.1214/12-aos1080