{"paper":{"title":"Self-separated and self-connected models for mediator and outcome missingness in mediation analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Elizabeth A. Stuart, Fan Yang, Grace V. Ringlein, Razieh Nabi, Trang Quynh Nguyen","submitted_at":"2024-11-11T18:43:09Z","abstract_excerpt":"Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on identification. We first consider self-separated missingness models where identification is achieved by conditional independence assumptions. This model class is somewhat limited as it is constrained by the need to remove a certain number of connections from the model. We then turn to self-connected missingness models where identification relies on information from shadow variables. This model class turns out to contai"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"92acb8823fea37a5eb5a6deac9e6a402d036c8c3a32dd5c0b9c8ec784223682e"},"source":{"id":"2411.07221","kind":"arxiv","version":2},"verdict":{"id":"cf89d3e6-63a5-4686-9a50-a7c344398a6f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-23T17:22:59.362603Z","strongest_claim":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory.","one_line_summary":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold.","pith_extraction_headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.07221/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":38,"sample":[{"doi":"","year":1986,"title":"The Moderator - Mediator Variable Distinction in Social Psychological Research : Conceptual , Strategic , and Statistical Considerations","work_id":"f91d87c6-1792-4d8b-958e-53abd275e013","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID -19 vaccine correlates of protection","work_id":"1e739fdb-857e-4ac9-8f66-2b7fa98f4c99","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, and James M. 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