Pith Number

pith:MJA5W4CW

pith:2025:MJA5W4CWWBM7VIFIOS3BINLMTV

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Causal Inference with Missing Exposures and Missing Outcomes

Atukunda Mucunguzi, Carina Marquez, Edwin D. Charlebois, Elijah Kakande, Florence Mwangwa, Kirsten E. Landsiedel, Laura B. Balzer, Moses R. Kamya, Rachel Abbott

Causal effects with missing exposures and baseline outcomes can be identified using counterfactual strata effects.

arxiv:2506.03336 v4 · 2025-06-03 · stat.ME

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\usepackage{pith}
\pithnumber{MJA5W4CWWBM7VIFIOS3BINLMTV}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

We demonstrate how the causal inference framework can be extended to missing exposures and to incorporate missingness on the baseline outcome, which induces missingness on the population of interest, using Counterfactual Strata Effects.

C2weakest assumption

The identification results rely on standard missing-at-random assumptions and no unmeasured confounding conditional on observed covariates, which are invoked when defining the counterfactual strata and applying TMLE; these are not verified in the provided abstract.

C3one line summary

The paper introduces counterfactual strata effects to identify causal estimands under missing exposures, missing baseline outcomes, and missing follow-up outcomes, demonstrated on the SEARCH-TB study with TMLE and Super Learner.

Receipt and verification

First computed	2026-06-19T16:09:49.578087Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6241db7056b059faa0a874b614356c9d6bd215727631be23d81f23e0250371e3

Aliases

arxiv: 2506.03336 · arxiv_version: 2506.03336v4 · doi: 10.48550/arxiv.2506.03336 · pith_short_12: MJA5W4CWWBM7 · pith_short_16: MJA5W4CWWBM7VIFI · pith_short_8: MJA5W4CW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/MJA5W4CWWBM7VIFIOS3BINLMTV \
  | 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: 6241db7056b059faa0a874b614356c9d6bd215727631be23d81f23e0250371e3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c6d4f2b50a27ecd3c11e348eb78730e3d26c7e25a87dc0b872bb93181326bf51",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2025-06-03T19:28:57Z",
    "title_canon_sha256": "0cab8c2f51e19c9dd3459856a41c7bc0f187526c909cc64763ce8d8a407cf1a9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2506.03336",
    "kind": "arxiv",
    "version": 4
  }
}