Pith Number

pith:7N234CZX

pith:2026:7N234CZXRCN4ARJHULQISJLDCB

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Toward a practical handbook for choosing among causal inference methods in non-randomized studies with binary outcomes: A simulation study for applied researchers

Adri\'an Aurensanz-Crespo, Crist\'obal M Rodr\'iguez-Leal, Jes\'us As\'in, Jorge Castillo-Mateo, Jos\'e M Ram\'irez, Rosario Susi, Teresa P\'erez

Simulations show the best causal method for binary outcomes depends on sample size, treatment share, and outcome prevalence

arxiv:2605.13388 v1 · 2026-05-13 · stat.ME · stat.AP

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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

The performance of propensity score full matching, inverse probability weighting, G-computation, and targeted maximum likelihood estimation depends on sample size, proportion treated, outcome prevalence, treatment effect magnitude, target estimand, and assumption violations; the resulting handbook provides practical guidance for method selection in binary-outcome settings.

C2weakest assumption

That the chosen simulation scenarios and performance metrics adequately cover the range of real-world data characteristics and that the four methods were implemented without simulation-specific biases that would not appear in actual data analysis.

C3one line summary

A simulation study compares four causal methods for binary treatment and outcome and delivers a selection handbook tested on two real medical datasets.

References

41 extracted · 41 resolved · 0 Pith anchors

[1] Per-Protocol Analyses of Pragmatic Trials.New England Journal of Medicine.2017;377:1391–1398 2017

[2] What if.Chapman and Hall/CRC, 2020 2020

[3] Identifiability, Exchangeability, and Epidemiological Confounding.Inter- national Journal of Epidemiology.1986;15:413–419 1986

[4] Matching Methods for Causal Inference: A Review and a Look Forward.Statistical Science: a Review Journal of the Institute of Mathematical Statistics.2010;25(1):1 2010

[5] Introduction to Computational Causal Inference Using Reproducible Stata, R, and Python Code: A Tutorial.Statistics in Medicine.2022;41:407–432 2022

Receipt and verification

First computed	2026-05-18T02:44:47.741461Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fb75be0b37889bc04527a2e0892563107429e9683c56297f74312c15132c4ac8

Aliases

arxiv: 2605.13388 · arxiv_version: 2605.13388v1 · doi: 10.48550/arxiv.2605.13388 · pith_short_12: 7N234CZXRCN4 · pith_short_16: 7N234CZXRCN4ARJH · pith_short_8: 7N234CZX

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7N234CZXRCN4ARJHULQISJLDCB \
  | 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: fb75be0b37889bc04527a2e0892563107429e9683c56297f74312c15132c4ac8

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "308b609a1afbb13369200abd7a5a46efd4cce4d6e5d52fd29d417188db4cf9b3",
    "cross_cats_sorted": [
      "stat.AP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-13T11:46:47Z",
    "title_canon_sha256": "37597c25dc8daedaea774255bd805ff84769a565cd73d83269c27ce7c8db0ad9"
  },
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  "source": {
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    "kind": "arxiv",
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}