Pith Number

pith:NNT3O7CG

pith:2024:NNT3O7CGW4HENX4UUA62YX7FIS

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Self-separated and self-connected models for mediator and outcome missingness in mediation analysis

Elizabeth A. Stuart, Fan Yang, Grace V. Ringlein, Razieh Nabi, Trang Quynh Nguyen

Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.

arxiv:2411.07221 v2 · 2024-11-11 · stat.ME

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\pithnumber{NNT3O7CGW4HENX4UUA62YX7FIS}

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

38 extracted · 38 resolved · 0 Pith anchors

[1] The Moderator - Mediator Variable Distinction in Social Psychological Research : Conceptual , Strategic , and Statistical Considerations 1986

[2] Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID -19 vaccine correlates of protection 2021

[3] Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, and James M. Robins. Identification In Missing Data Models Represented By Directed Acyclic Graphs . In Proceedings of The 35th Uncertainty in Artificial 2020

[4] Cole and Constantine E 2009 · doi:10.1097/ede.0b013e31818ef366

[5] Ghazaleh Dashti, Katherine J 2024

Receipt and verification

First computed	2026-06-19T16:10:29.010764Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750

Aliases

arxiv: 2411.07221 · arxiv_version: 2411.07221v2 · doi: 10.48550/arxiv.2411.07221 · pith_short_12: NNT3O7CGW4HE · pith_short_16: NNT3O7CGW4HENX4U · pith_short_8: NNT3O7CG

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4dbaa8164694c12c46754bf7ca83cf38826cef0140f346aadf3a514b39b8cd37",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2024-11-11T18:43:09Z",
    "title_canon_sha256": "0969273f21c63bd92fada3ade50ccd44c7f1f46c6d935f4b6f9e96f1e2c27b64"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2411.07221",
    "kind": "arxiv",
    "version": 2
  }
}