{"paper":{"title":"Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Propensity score propagation achieves nominal coverage for design-based inference when propensity scores are unknown and estimated from data.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Siyu Heng, Yanxin Shen, Zijian Guo","submitted_at":"2026-01-19T15:32:09Z","abstract_excerpt":"Design-based inference, also known as randomization-based or finite-population inference, provides a principled framework for trustworthy statistical inference by attributing randomness solely to the design mechanism (e.g., treatment assignment, survey sampling, or missingness), without imposing super-population distributional or modeling assumptions on outcome data. From Fisher's and Neyman's seminal work to the recent resurgence of design-based inference, this perspective has played a central role in causal inference, survey sampling, and missing data analysis. However, a fundamental obstacl"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Theoretical and simulation studies show that the proposed framework achieves nominal coverage, even in settings where conventional approaches exhibit substantial under-coverage.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the regeneration-and-union procedure correctly propagates uncertainty from propensity score estimation into the design-based inference without violating the finite-population properties or introducing bias that the theory does not account for.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Propensity score propagation is a general framework that achieves nominal coverage for design-based inference by propagating uncertainty from both parametric and nonparametric propensity score estimates via a regeneration-and-union procedure.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Propensity score propagation achieves nominal coverage for design-based inference when propensity scores are unknown and estimated from data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"de27d872e12b3301cd1150bd37a5ae64541a33032ac629ea4e8cafdabb1240d1"},"source":{"id":"2601.13150","kind":"arxiv","version":3},"verdict":{"id":"a6653553-b12d-46cc-bcae-bb498797dd14","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T13:06:43.567345Z","strongest_claim":"Theoretical and simulation studies show that the proposed framework achieves nominal coverage, even in settings where conventional approaches exhibit substantial under-coverage.","one_line_summary":"Propensity score propagation is a general framework that achieves nominal coverage for design-based inference by propagating uncertainty from both parametric and nonparametric propensity score estimates via a regeneration-and-union procedure.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the regeneration-and-union procedure correctly propagates uncertainty from propensity score estimation into the design-based inference without violating the finite-population properties or introducing bias that the theory does not account for.","pith_extraction_headline":"Propensity score propagation achieves nominal coverage for design-based inference when propensity scores are unknown and estimated from data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.13150/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"}