{"paper":{"title":"Learning Measurement Models for Unobserved Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Clark Glymour, Peter L. Spirtes, Ricardo Silva, Richard Scheines","submitted_at":"2012-10-19T15:08:15Z","abstract_excerpt":"Observed associations in a database may be due in whole or part to variations     in unrecorded (latent) variables. Identifying such variables and their causal     relationships with one another is a principal goal in many scientific and     practical domains. Previous work shows that, given a partition of observed     variables such that members of a class share only a single latent common cause,     standard search algorithms for causal Bayes nets can infer structural relations     between latent variables. We introduce an algorithm for discovering such     partitions when they exist. Unique"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2516","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}