How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Population PC causal-discovery algorithm
Definition
Input: A finite vertex set , a perfect conditional-independence oracle for a law in the causally sufficient discovery regime, and a fixed total order of . For every adjacent pair and conditioning set tested below, the oracle returns whether and are conditionally independent given .
Output: An undirected skeleton , a separator record for each deleted edge, and a partially directed graph .
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Initialize to the complete undirected graph on and set the level .
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In the fixed lexicographic order of unordered vertex pairs, process each pair that is still an edge of . First test every -element subset of in lexicographic order; then test every -element subset of in lexicographic order. On the first oracle result of conditional independence given a tested set , delete , record , and do not test that pair again. If no set separates the pair, leave it adjacent. Adjacency sets are read from the current graph, including deletions already made at level .
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If at least one remaining edge has an endpoint with at least neighbors other than the other endpoint, increment and repeat step 2. Otherwise stop the skeleton search.
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Copy the completed skeleton to a separate graph . For each unshielded triple in , orient its incident edges in as exactly when , retaining any orientations already made. Keep unchanged.
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Repeatedly orient edges of to closure under the following background-free rules, using fixed lexicographic order on rule instances:
- R1: If and are nonadjacent, orient as .
- R2: If is undirected and , orient as .
- R3: If , , and are undirected, , , and are nonadjacent, orient as .
Do not apply a background-knowledge rule. Return , the separator record, and .
Depends on
Used by
- A complete three-vertex skeleton has several equivalent DAGs Example
- A hidden common cause is observationally indistinguishable from an edge Example
- PC distinguishes a chain from an unshielded collider Example
- The background-free Meek rules complete a CPDAG Lemma
- High-dimensional consistency of Gaussian PC structure learning Theorem
- The PC algorithm recovers the oracle CPDAG Theorem
Dependency tree · two levels
11 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Kalisch and Bühlmann, Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm, §§2.2.1 and 2.3 (standard reference, not scraped)
- Meek, Causal Inference and Causal Explanation with Background Knowledge, §2.1 (standard reference, not scraped)