{"paper":{"title":"Mimic and Classify : A meta-algorithm for Conditional Independence Testing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arman Rahimzamani, Himanshu Asnani, Karthikeyan Shanmugam, Rajat Sen, Sreeram Kannan","submitted_at":"2018-06-25T21:24:52Z","abstract_excerpt":"Given independent samples generated from the joint distribution $p(\\mathbf{x},\\mathbf{y},\\mathbf{z})$, we study the problem of Conditional Independence (CI-Testing), i.e., whether the joint equals the CI distribution $p^{CI}(\\mathbf{x},\\mathbf{y},\\mathbf{z})= p(\\mathbf{z}) p(\\mathbf{y}|\\mathbf{z})p(\\mathbf{x}|\\mathbf{z})$ or not. We cast this problem under the purview of the proposed, provable meta-algorithm, \"Mimic and Classify\", which is realized in two-steps: (a) Mimic the CI distribution close enough to recover the support, and (b) Classify to distinguish the joint and the CI distribution."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09708","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"}